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Expert help when you need it- Q1:please address this question: 1.Compare and contrast symptoms and circuits in depression with symptoms in circuits in mania. 2.Discuss the neurotransmitters implicated in mood disorders. For this discussion, place particular emphasis on the monoamine hypothesis of depression.See Answer
- Q2:Instructions: Need to Do the peer review for the wiki article about 300 - 450 words./n 1/23/24, 9:18 AM <> Code 180D-FW-2023/ Knowledge-Base-Wiki Issues Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Pull requests Actions Introduction Projects Application of Kalman Filter in Neural Signal Decoding Edit New page Hongchang Kuang edited this page 2 weeks ago. 6 revisions Hongchang Kuang Word count: 2610 (including Latex formula and HTML code) Wiki Application of Kalman Filter in Neural Signal Decoding Security https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding R Jump to bottom Have you imagined a world that we could control things without moving our fingers? Will you fancy playing video games or driving your cars just with your brain? I have to tell you that this fantasy is no longer impossile with the help of Brain Machine Interface (BMI). In recent decades, as technology advancing and social ethics system maturing, a heated debate has been triggered over the usage of BMI. Some argues that current innovations are still not applicable for widely marketing products. However, most engineers and neuroscientists are excited by technological progress that facilitates brain mapping, the most sanguine of them comparing their growing ability to tremendous advances that led to the unimaginable success of the BMI projects. Companies like NeuralLink and Paradromics, as well as government agency have invested on the projects to test the possibilities and work on the real world applications [1]. This articles briefly introduces one of the prevailing technology in BMI: the application of Kalman filter in neural signal decoding, discussing its advantages and potential improvements, and what makes it robust and powerful that could possibly allows BMI to enhance our daily life. 1/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Backgrounds in Neuroscience Before we get to the real technique, a simple background in neuroscience is neccessary to unravel you from the potential confusion in the world of jargons, since the mind, with our brain in general, is our main subject of research. The main study theme of neuroscience is to link molecules to mind. Human brain is a highly integrated network of approximately 100 billion individual nerve cells. Therefore, to fully understand the mechanism behind the BMI devices, a brief understanding of how neurons are organized into signaling pathways and how they communicated is necessary. We will introduce how brain functions from three different levels: brain, neuron, and neural signal (action potential). Further readings in related areas are also encouraged. The Brain and Behavior What does our brain look like? Surprisingly, it is complex and ordered at the same time. The central nervous system (CNS) is bilateral and symmetrical. Studies on brain with modern imaging techniques discovers that different regions on brain are specialized for different functions. As you may have gussed, for BMI to decode human cognitive information, the most important part that we would like to focus on is the cerebral cortex, where brain operations that are responsible for human cognitive abilities occur. As shown in the graph, It consists of four anatomically distinct lobes with different functionalities, including planning future actions, hearing, learning, vision, etc. With the concept of functional engineering prevailing in neuroengineering, it is natural to focus on a specific area on cerebral cortex to gather information of neuron activity. Therefore, to achieve fancy operations like controlling the cursor with our mind, for example, we will mainly focus on motor cortex in frontal lobe, which is responsible for planning future actions and the control of movement. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 2/15 1/23/24, 9:18 AM A Neurons B Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Motor cortex (Precentral gyrus) Central sulcus Frontal lobe Lateral sulcus Arcuate fasciculus Broca's area Vocalization region of motor area Parietal lobe Temporal lobe Somatic sensory cortex (Postcentral gyrus) Primary auditory cortex Occipital lobe Angular gyrus Wernicke's area Visual cortex Figure 1-4 The major areas of the cerebral cortex are shown in this lateral view of the of the left hemisphere. A. Outline of the left hemisphere. B. Areas involved in language. Wernicke's area processes the auditory input for language and is important to the understand- ing of speech. It lies near the primary auditory cortex and the angular gyrus, which combines auditory input with information from other senses. Broca's area controls the production of in- telligible speech. It lies near the region of the motor area that controls the mouth and tongue movements that form words. Wernicke's area communicates with Broca's area by a bidirec- tional pathway, part of which is made up of the arcuate fasci- culus. (Adapted from Geschwind 1979.) Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 3/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki What is the basic unit of our brain and what makes it complex? We have to give credit to these simple but powerful biological creatures: nerve cells (neurons). Just like VLSI transistors, even though they have relatively basic morphology and architecture, approximately 10^11 neurons in the brain can support long-ranged and intricate anatomical circuits, where the "complexity" arises from. Neuron have four regions: cell body (soma), dendrites, axon, and presynaptic terminals. The main component axon is what we should pay special attention to: it conveys signals (action potential) to other neurons in long distance (0.1mm - 3m), while ensure the signals propagate without distortion or failure and preserving its shape at very high speed (1 - 100m/s). Compared to transmission line in our real life, the efficiency and accuracy of neural signal transmission are mind-blowing. Two neurons can also communicate at the synapse chemically using neurotransmitters. Neurons, whose structure diagram is attached below, are the most powerful building blocks of the most complicared circuits in the world. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 4/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Excitatory terminal fiber of an axon -Presynaptic cell- .....Postsynaptic cells- Inhibitory terminal fiber of an axon Axon (initial segment) Node of Ranvier Myelin sheath- Neural Signal (Action Potential) Axon Apical dendrites Cell body -Nucleus Basal dendrites Axon hillock Presynaptic terminal Synaptic cleft -Postsynaptic dendrite Act the brai These s nervous great va on our odorant vey info carry ir other k https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding convey the for travels patterns creates smell, a To conduc ing she lar inte sulated become nation i ter 9. Ne branch rons. T known called t Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] Synapse 5/15See Answer
- Q3:1. Too Technical: It may be challenging for readers without a background in neuroscience or signal processing. Add some signal processing knowledge (overview everyday example). a. Mobile Communications b. Health Monitoring and Medical Imaging c. Automotive Safety and Autonomous Vehicles 2. Focused solely on Kalman Filters: Provide some alternative or similar methods in neural signal decoding and give some reasons/examples why Kalman filters in some cases are the best way to decode the neural signal. 3. Add code and mathematical proof and provide some background knowledge related to Kalman Filters Neural Network, https://www.ibm.com/docs/en/spss-modeler/18.0.0?topic=networks-neural-mod el Math on Kalman filter https://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.p df Code: https://github.com/KordingLab/Neural_Decoding/blob/master/Examples_kf_dec oder.ipynb 4. More practical examples: a. GPS or navigation for ships, control of vehicles, and aircraft(dynamical position) b. Tracking object c. Economics d. Computer Vision Applications 5. State some restrictions or drawbacks on Kalman Filters. a. It assumes that both the system and observation model equations are linear, which is not realistic in many real-life situations. b. It assumes that the state belief is Gaussian distributed.See Answer
- Q4:1) 3-part question: A Please draw a schematic of a multipolar neuron receiving information from adjacent neurons and propagating the signal to a bipolar neuron which will continue the signal. Upload your drawing here in part A, after you have finished parts B and C. B. On the schematic, please label all the parts of the drawn neurons using numbers and define the numbers bellow. C. On the schematic, please label all events happening with letters and provide a detailed description below of each event occurring from the beginning to the end of the process.See Answer
- Q5:2) Which of the following statement is not true of myasthenia gravis? A. There is progressive muscle weakness. B. Emergence of cognitive dysfunctions. C. antibodies bind to the acetylcholine receptors and cause underactivity of acetylcholine. D. Sustained motor activity helps identify the muscle weakness.See Answer
- Q6:3) Emergence of myelin plaques in the CNS white matter could result in the following A Parkinson disease B. cerebral palsy C. multiple sclerosis D. Huntington chorea E. Both A and C F. Both B and DSee Answer
- Q7:4) One of the most difficult barriers to permeate is the blood-brain barrier. This highly selective Assignment 1 membrane owes this to A fibroblasts B. specialized capillaries C. microglia D. astrocytes E. oligodendroglia CASD2277 February 6, 2024See Answer
- Q8:5) A patient was diagnosed with a tum or associated with the myelin form ing glia in the PNS. Which of the following cells is likely involved? A astrocyte cells B. microglia cells C. Schwann cells D. oligodendroglia cells E. ependym al cellsSee Answer
- Q9:6) Which of the following is found in higher concentrations inside the neuron during the cellular resting period? A Na B. Ca C. P D. K E. C1See Answer
- Q10: Assignment #5 Chapter 17: Crania Nerves Week 15: May 8, 2024 A. Looking at this image, from which perspective are we viewing the brain? (5 points) B. Fill in the description of each of the 12 cranial nerves: Origin, structures supplied, type of fibers, functions and circle on the right side of the box whether the nerve is: "S" Sensory, "M" Motor, and "B" Both/Mixed (see example below). (95 points) OLFACTORY: CN I ORIGIN: Root of nasal cavity STRUCTURES SUPPLIED: Nasal cavity, M FIBERS: SVA B FUNCTIONS: Smell OPTIC: CN II ORIGIN: OCULOMOTOR: CN III S ORIGIN: SS: M SS: FIBERS: B FIBERS: FUNCTIONS: FUNCTIONS: TROCHLEAR: CN IV TRIGEMINAL: CN V ORIGIN: S SS: M ORIGIN: FIBERS: B SS: FUNCTIONS: FIBERS: FUNCTIONS: ABDUCENS: CN VI ORIGIN: S SS: M FIBERS: B FUNCTIONS: FACIAL: CN VII ORIGIN: SS: FIBERS: FUNCTIONS: GLOSSOPHARYNGEAL: CN IX M ORIGIN: B SS: S M B VESTIBULOCOCHLEAR: CN VIII ORIGIN: S S SS: M M FIBERS: B B FUNCTIONS: VAGUS: CN X ORIGIN: S 55: 3 n S M FIBERS: B FIBERS: B FUNCTIONS: FUNCTIONS: HYPOGLOSSAL: CN XII ACCESSORY: CN XI ORIGIN: S ORIGIN: SS: M S SS: FIBERS: B M FIBERS: FUNCTIONS: B FUNCTIONS:See Answer
- Q11: WHAT TO DO Need to do it in 250-300 words Video link- https://www.youtube.com/watch?v=e9jSeRBJH8U No need to add referencesSee Answer
- Q12:Question Need to do the peer review of wiki article attached in 300-450 words Instructions: Answer all the questions following the instructions. Strictly do not use AI for solving questions. The solution should be free of Plagiarism. Solution to be formatted in APA and use appropriate references with in-text citations in APA./n1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki 180D-FW-2023 / Knowledge-Base-Wiki <> Code Issues !* ) Pull requests Actions Projects Wiki Security Application of Kalman Filter in Neural Signal Decoding Edit New page Hongchang Kuang edited this page 2 weeks ago · 6 revisions Jump to bottom Application of Kalman Filter in Neural Signal Decoding Hongchang Kuang Word count: 2610 (including Latex formula and HTML code) Introduction Have you imagined a world that we could control things without moving our fingers? Will you fancy playing video games or driving your cars just with your brain? I have to tell you that this fantasy is no longer impossile with the help of Brain Machine Interface (BMI). In recent decades, as technology advancing and social ethics system maturing, a heated debate has been triggered over the usage of BMI. Some argues that current innovations are still not applicable for widely marketing products. However, most engineers and neuroscientists are excited by technological progress that facilitates brain mapping, the most sanguine of them comparing their growing ability to tremendous advances that led to the unimaginable success of the BMI projects. Companies like NeuralLink and Paradromics, as well as government agency have invested on the projects to test the possibilities and work on the real world applications [1]. This articles briefly introduces one of the prevailing technology in BMI: the application of Kalman filter in neural signal decoding, discussing its advantages and potential improvements, and what makes it robust and powerful that could possibly allows BMI to enhance our daily life. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 1/15 Q 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki Backgrounds in Neuroscience Before we get to the real technique, a simple background in neuroscience is neccessary to unravel you from the potential confusion in the world of jargons, since the mind, with our brain in general, is our main subject of research. The main study theme of neuroscience is to link molecules to mind. Human brain is a highly integrated network of approximately 100 billion individual nerve cells. Therefore, to fully understand the mechanism behind the BMI devices, a brief understanding of how neurons are organized into signaling pathways and how they communicated is necessary. We will introduce how brain functions from three different levels: brain, neuron, and neural signal (action potential). Further readings in related areas are also encouraged. The Brain and Behavior What does our brain look like? Surprisingly, it is complex and ordered at the same time. The central nervous system (CNS) is bilateral and symmetrical. Studies on brain with modern imaging techniques discovers that different regions on brain are specialized for different functions. As you may have gussed, for BMI to decode human cognitive information, the most important part that we would like to focus on is the cerebral cortex, where brain operations that are responsible for human cognitive abilities occur. As shown in the graph, It consists of four anatomically distinct lobes with different functionalities, including planning future actions, hearing, learning, vision, etc. With the concept of functional engineering prevailing in neuroengineering, it is natural to focus on a specific area on cerebral cortex to gather information of neuron activity. Therefore, to achieve fancy operations like controlling the cursor with our mind, for example, we will mainly focus on motor cortex in frontal lobe, which is responsible for planning future actions and the control of movement. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 2/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki A Motor cortex (Precentral gyrus) Somatic sensory cortex (Postcentral gyrus) Central sulcus Parietal lobe Frontal lobe Occipital lobe Temporal lobe Lateral sulcus B Arcuate fasciculus Angular gyrus Broca's area - Visual cortex Vocalization region of motor area Wernicke's area Primary auditory cortex Figure 1-4 The major areas of the cerebral cortex are shown in this lateral view of the of the left hemisphere. A. Outline of the left hemisphere. B. Areas involved in language. Wernicke's area processes the auditory input for language and is important to the understand- ing of speech. It lies near the primary auditory cortex and the angular gyrus, which combines auditory input with information from other senses. Broca's area controls the production of in- telligible speech. It lies near the region of the motor area that controls the mouth and tongue movements that form words. Wernicke's area communicates with Broca's area by a bidirec- tional pathway, part of which is made up of the arcuate fasci- culus. (Adapted from Geschwind 1979.) Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] Neurons https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 3/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki What is the basic unit of our brain and what makes it complex? We have to give credit to these simple but powerful biological creatures: nerve cells (neurons). Just like VLSI transistors, even though they have relatively basic morphology and architecture, approximately 10^11 neurons in the brain can support long-ranged and intricate anatomical circuits, where the "complexity" arises from. Neuron have four regions: cell body (soma), dendrites, axon, and presynaptic terminals. The main component axon is what we should pay special attention to: it conveys signals (action potential) to other neurons in long distance (0.1mm - 3m), while ensure the signals propagate without distortion or failure and preserving its shape at very high speed (1 - 100m/s). Compared to transmission line in our real life, the efficiency and accuracy of neural signal transmission are mind-blowing. Two neurons can also communicate at the synapse chemically using neurotransmitters. Neurons, whose structure diagram is attached below, are the most powerful building blocks of the most complicared circuits in the world. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 4/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki Act the brai These nervous great va on our Apical dendrites odorant vey inf carry ir other k convey the forı travels patterns Inhibitory terminal creates fiber of an axon smell, a To i Cell body conduc Excitatory terminal fiber ing she of an axon lar inte: sulated - Nucleus become nation i ter 9. Basal dendrites Axon Ne (initial segment) hillock Axon branch rons. T. known Node of Ranvier called t -Presynaptic cell- Myelin sheath Axon Presynaptic terminal Synaptic cleft Synapse ..... Postsynaptic cells -> Postsynaptic dendrite Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] Neural Signal (Action Potential) https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 5/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki Is the neural signal similar to the digital signal used for communication in the real life? Actually, they are nearly identical. Action potentials are the signals by which brain receives, analyzes and conveys information. Even though they are highly stereotyped just like digital signal (0 and 1), they are able to convey all the information that the brain may need, including vision, audition, emotion, etc. However, unlike the 0s and 1s in digital signals, since they do not have distinguishable shapes, all the information is preserved in the path that the signal travels and pattern of action potentials. The maximum magnitude of action potential can be up to 40 mV, and their frequency is about 500 Hz. On the other hand, it is also their highly stereotyped shape makes decoding them feasible. Just like displacement and velocity, in neural signal decoding, we are more interested in the "velocity" of action potential: firing rate, which is the number of spikes of action potentials per unit time. With this concept, we are able to have a criterion of how active a neuron is. -+40 3 mV E -70 Figure 2-3 This historic tracing is the first published intra- cellular recording of an action potential. It was obtained in 1939 by Hodgkin and Huxley from the squid giant axon, using glass capillary electrodes filled with sea water. Time marker is 500 Hz. The vertical scale indicates the potential of the internal electrode in millivolts, the sea water outside being taken as zero potential. (From Hodgkin and Huxley 1939.) Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] Methods With all the knowledge of neuroscience infused in your barin, I think your neurons should be ready for the real methods of decoding the neural data. Sit back, and I will walk you through this wonderful and inspiring process. Motor Prothesis Concept https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 6/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki This architecture of our decoder is simple but elegant. As shown in the graph, the whole BMI system consists of multiple components, including signal detection, decode algorithm, and feedback. In this section, we will focus on continuous decoding of neural signal in hand movement (motor prosthesis). Image that you would like to grab the water bottle on the table. Your brain will go over two stages: planning and movement. In the planning stage, it need to plan the according trajectories precisely. In the following movement activity, it also needs to control the acceleration and speed of your arm. In general, the movement activity follows the plan activity, and is specially tuned for direction and speed of arm movement. As a result, we will be mainly concerned with the neural movement activity and the corresponding kinematics like hand position, velocity, acceleration, etc. Cortex Neural signals Electrode Spike Detection array Neuron number ..... Visual feedback Time 1 Sensory feedback 1 + Decode algorithm Motor prosthesis Control signals ABCDEFG HIJKLMN OPQRSTU VWXYZ Communication prosthesis Source: Slides from EC ENGR C143A "Neural Signal Processing" Lecture 14 by professor Jonathan Kao [3] Decoding Data and Notation We need to know what are we going to decode at first and what the experiment looks like. Let us set up the following experimental context: a monkey is asked to perform a task. It needs to move its hand to the cursor on the screen. When the cursor appears, it cannot move. As soon as it disappear, the monkey should move its hand to the destination. We call this whole process a reach. Apparently, for the reaching task, we have two sources of data to consider: 1. Observed kinematics (dimensionality M): https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 7/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki p x p y k Ck = v x k v y k ⎣ 1 pr and pl represent the position of hand at time k (for x and y coordinates). Respectively, vg and represent the x and y velocity of hand at time k. We ignore the acceleration and add a 1 to the end for calculation simplicity and matching the dimension. 2. Neural activity (dimensionality N) ,1 ⎤ ⎦ ⎡ yk ⎤ 2 y k yk = ⋮ „N yk 1 ⎦ y" represents the neuron n's firing rate at time k. In our case N = 96, which means that we decode neural signals from 96 neurons at the same time. We add a 1 to the end for calculation simplicity and matching the dimension. We will further denote the decoded kinematics as Xx. For convenience, for the total period of time of these activities, we can also define: X = [x1 x2 ... CK] Y = [y1 y2 ... yK] where K is the total number of data points. If the time interval between two points is At, thus the total period of time of these activity will be T = KAt. General Ideas in Decoding The general idea of the decoding operation is straightforward: decode th kinetics information from neural activities, which means to infer X from Y. You might think this sounds easy. One simple method is that, if we could assume that their relation is linear, we can simply fit a line to them using least square since we have both observed data for X and Y: X = LY Basically is to minimize: 1 |2Ck - Lykl12 k=1 K https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 8/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki With our knowledge in linear algebra, we get: L= XYT(YYT)-1 Now we have this L. Concretely, say that we have a new neural signal data k, then we can decode the kinetics &k using: ֏k = Lýk Problem solved! This is what we called Optimal Linear Estimator (OLE). Is this the final method that we should concern? Of course not! Unfortunately, as you may guess, its accuracy and robustness are not very ideal. Obviously, we have ignore something in this case. First, for the adjacent positions, say &n and &n+1, there must be some relation between them; second, even though we introduce the concept of velocity, we did not apply any physics law to exploit them. Well, the first problem seems easy to solve: we can simply take history into account by doing something like: ∑ P p=0 Rewrite it in matrix form: Xw =LwYw We can find Lw using least square as well: Lw =XUYT (YaYT)-1 Seems more advanced, doesn't it? This technique is called Wiener Filter. We enhance the OPE by considering data from the history. But what about the second problem? it's finally time to welcome Kalman Filter to the stage. Decoding with Kalman Filter The main concern for now is, we have additional information we have not used in our decoding process: the law of physics, since we understand the the positions of the arm are the integration of its velocity (we will be in real trouble if they are not). The natural way to incorporate this is to use Kalman Filter. If we add one more equation to the original OLE, we can get: @k+1 = Axk yk = Cxk https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 9/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki The first equation is called state update process or dynamic process, where xk, the kinematics of the hand, is the state. A is the dynamic matrix where we can encode the law of physics. The second equation is observation process. yk is just the observation of neural signal. This inverse OLE equation helps us link the neural signal yk and kinematics xk. If you recall your signals and systems class, these two equations together is called Linear Dynamical System. In real life, we have to add some random noise to them: Xk+1 = Axk + Wk yk = Cxk + qk We can simply assume that they are gaussian noise. Zero mean and covariance matrix W and Q will be good in our case: Wk ~ N (0, W) qk ~N (0,Q) So how do we get the optimal estimator of ck from those two equations? Under certain assumption (linearity and gaussian noise), there is a recursive solution to this called Kalman Filter The spoiler is, we will reach to some kind of equation like this: ֏k = M1ºk-1 + M2yk Apparently, we take all factors into account: history, law of physics, and the neural signal. All of them have some information of what the newly decoded data should be. So the goal is to find two matrices M1 and M2. They are the function of A, C, W, Q. What is Kalman Filter actually doing? In one dimension, we need to find an estimator kl, .,k = E(xk |31, ... , yk) In other words, this means "we want to find the expected value of hand position at time k given all my observations of neural signal from time 1 to k". We also need to know the variance of it to estimate how much we trust this value (in multi-dimension this will be 2): "kl1 .. .. ,k = Var (kly1, ... , yk) In general, we would write the distribution of xkly1, ... , yk and then just take its expected value. This seems to be difficult, but we have a recursive solution provided by Kalman Filter. We will ignore the tedious derivation of it here. The process will be: 1. Initialize: 2010 = 0 2. Estimation updated process: Recursively calculate, until convergence of Kk: 2klk-1 = AZk-1|k-1AT + W https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 10/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki Dkk = 2k-1|k-1 - 2k-1|k-1CT(CZk-1|k-1CT)-1CZk-1|k-1 T T ) We called the converged value . and Ko, and Kk is called the Kalman Gain After some more magic, we can calculate M1 and M2 by M1 = A -KCA M2 = Koo What about A, C, W, Q themselves? We need to calculate them using maximum likelihood method in machine learning. What we need to do is to maximize the following likelihood with parameters set 0: L = P(k> Uk) k=1 0 = A, W, C, Q) This means that, with the parameters set 0, we need those obaservations data to appear at the highest possibility. We can do this by taking the derivative of logarithm of L with respective to A, C, W, Q. Then simply set it to zero. What we will finally get is: A= X:,2:endX+ :,1:end-1 C = YX+ W = K-1 1 (X:,2:end - AX:,1:end-1)(X:,2:end - AX;,1:end-1)" Q = = (Y-CX)(Y -CX)T We also need to make sure that A obeys law of physics. With all these parameters, we should be able to decode xk using the formula we first propose. The final results of the decoding is shown in the graph on the left. On its right is the actually movement of all the reach in the experiment marked by different colors, where x and y axis represent the coordinates of hand positions. Although they don't seem to be close, the analysis on loss function of squared error proved the results to be reliable, and in real life they will work with high accuracy if we are not very serious about fidelity. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 11/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki decoded positions 200 100 0 -100 -200 - -200 -150 -100 -50 0 50 100 150 200 hand positions 150 100 - 50 0 -50 -100 - -150 -100 -50 0 50 100 Source: EC ENGR C143A Spring 2023 HW6Q4 by Hongchang Kuang [4] https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 12/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding . 180D-FW-2023/Knowledge-Base-Wiki Wiki Conclusion I hope this method in neural signal processing is inspiring and fantastic. This article introduces the motivation of choosing Kalman Filter in decoding neural signal, and the basic mathematical recursion in arriving at the final answer. In general, when it comes to decode continous data, Kalman Filter is a powerful technique which incorporates factors like history, law of physics, and the characteristics of decoded data into the decoding process. It presents ideal accuracy and robustness. In our project, for example, we can choose to use Kalman Filter in smoothing the data collected from the IMU. Nevertheless, I cannot guarantee that Kalman Filter will be a panacea to all your problems: it also has some drawbacks. Kalman Filter can easily experience overshoot since it may weight too heavily on the history [3]. Some potential improvements in neural signal processing include rotating the velocity toward the destination (take angles into consideration) or use Closed Loop Decoder Adaptation (CLDA). In modern decoder with more advanced features, Deep Neural Network is also widely used. In the near future, we can expect that neural signal decoder would provide more advanced functionality like speech decoding, wating for you to explore further [5]. References [1] AI's Next Frontier: Are Brain-Computer Interfaces The Future Of Communication? Bernard Marr, Forbes. https://www.forbes.com/sites/bernardmarr/2023/08/11/ais-next-frontier-are-brain- computer-interfaces-the-future-of-communication/?sh=61d3d2b851d9 [2] Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [3] Slides from EC ENGR C143A "Neural Signal Processing" by professor Jonathan Kao [4] Homework answers from EC ENGR C143A "Neural Signal Processing" by Hongchang Kuang [5] SCIENTISTS SAY NEW BRAIN-COMPUTER INTERFACE LETS USERS TRANSMIT 62 WORDS PER MINUTE. Victor Tangermann, Futurism. https://futurism.com/neoscope/scientists-new-brain- computer-interface-type-62-words-per-minute + Add a custom footer - Pages 36 Find a page ... Home https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 13/15See Answer
- Q13: BIOL 334 Neurobiology Journal Article Perspective Papers This semester you will write two critical perspective papers on journal articles we read and discuss in class Journal Clubs. The purpose of these is to give you practice in summarizing the important points of scientific papers and in critically evaluating their quality. The first article we will discuss is: Chan et al., Nature 2007: "Rejuvenation" protects neurons in mouse models of Parkinson's disease. You will not write a paper on this article yet. Read it closely and be prepared to talk about key figures (see below) during our class Journal Club. After our discussion, I'll post an example perspective paper to guide you in your writing later. The main paper, supplementary text, and helpful background info are posted on Moodle under Journal Clubs. You will only be responsible for the figures in the main text (Fig. 1-5) and Supplementary Fig S1, S6, and S7. Don't worry about being familiar with the other figures. If you need a refresher on how approach a primary literature article, please see the other clearly- marked helpful resources I've posted to Moodle under Journal Clubs. For all our papers, I recommend reading the paper 2-3 times before beginning your assignment. Please feel free to discuss the papers with each other. If there are things you don't understand, ask questions! – to each other or to me. You should also do some digging on your own. However, assignments should be written individually in your own words (also do not quote or paraphrase from the paper). Your Perspective Paper will have two main parts (see below for details): 1. Summary statement: ~1 page 2. Perspective statement on one of the two options below (choose one: "A” or “B”): ~1 page A. paper quality B. implications/extensions of this work For your summary statement, address the following questions: What specific question/hypothesis were the researchers attempting to address? What are the most important experiments in the paper that addressed this question? (DO NOT try to discuss all the experiments—choose the ones that you feel are most important) What methods/techniques did the researchers use in these experiments? What conclusions did the authors draw based on these results? How did the conclusions address the researchers' original question/hypothesis? For your position sections, support your position as critically as possible, drawing upon information from class, the textbook, and other sources as appropriate. Depending on whether you are addressing the quality or implications, address the following questions: 2A. Paper quality How well were the experiments described? What (if anything) could the authors have done to make the experimental design clearer? How well did the data support the author's conclusions? What additional experiments or controls are needed for you to be fully convinced? How appropriate were the methods for the research question? Are there other techniques that would have been more appropriate to use? BIOL 334 Neurobiology 2B. Implications What further lines of inquiry are suggested by these studies? How did the work contribute to extending or transforming what was already known? What do you think is the next most logical experiment based on the new results? How would you design this experiment? What are specific ways in which the research can be extended to other model systems or other important questions that will make it even more impactful? Formatting Word document (NOT a PDF) titled as follows: Last name_paper (ex: Luth_Chan 2007) 2 pages maximum - keep your writing concise! I will not accept any paper longer than 2 pages. 1.5 spaced 12-point Arial 1-inch margins Item Hypothesis/research question, important results, and Points 15 conclusions, for 2-3 experiments are clearly and concisely stated Methods details for experiments are accurately described 10 Connections between the research question, experiments, and conclusions are made 5 Your position on the paper is clearly stated Critical analysis refers to specific elements of the paper Position is supported with course material and/or outside sources Suggestions for alternative methods (quality) or additional experiments (implications) are present Ideas logically presented with effective transitions 10 15 10 10 10 Reader can distinguish between the paper's findings and your interpretations and opinions 5 Few grammar or spelling mistakes 50 In-text citations and bibliography are in correct format 5 Total 100See Answer
- Q14:Question Journal Article Perspective Papers Do the summary statement of the paper attached in not more than one page For your summary statement, address the following questions: - What specific question/hypothesis were the researchers attempting to address? - What are the most important experiments in the paper that addressed this question? (DO NOT try to discuss all the experiments—choose the ones that you feel are most important) - What methods/techniques did the researchers use in these experiments? - What conclusions did the authors draw based on these results? - How did the conclusions address the researchers’ original question/hypothesis? Instructions: Answer all the questions following the instructions. Strictly do not use AI for solving questions. The solution should be free of Plagiarism. Solution to be formatted in APA and use appropriate references with in-text citations in APA./nRESEARCH | REPORTS 2. B. Bosworth, K. Zhang, "Evidence of Increasing Differential Mortality: A Comparison of the HRS and SIPP," Center for Retirement Research at Boston College Working Paper 2015-13 (2015). 3. R. Chetty et al., JAMA 10.1001/jama.2016.4226 (2016). 4. National Research Council, Committee on the Long-Run Macroeconomic Effects of the Aging U.S. Population, "The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses" (2015). 5. J. Pijoan-Mas, J. V. Ríos-Rull, Demography 51, 2075-2102 (2014). 6. H. Waldron, Soc. Secur. Bull. 67, 1-28 (2007). 7. H. Waldron, Soc. Secur. Bull. 73, 1-37 (2013). 8. J. Wilmoth, C. Boe, M. Barbieri, in International Differences in Mortality at Older Ages: Dimensions and Sources, E. M. Crimmins, S. H. Preston, B. Cohen, Eds. (National Academies Press, Washington, DC, 2011), pp. 337-372. 9. G. K. Singh, M. Siahpush, Int. J. Epidemiol. 35, 969-979 (2006). 10. M. Ezzati, A. B. Friedman, S. C. Kulkarni, C. J. Murray, PLOS Med. 5, e66 (2008). 11. C. J. Murray et al., PLOS Med. 3, e260 (2006). 12. H. Wang, A. E. Schumacher, C. E. Levitz, A. H. Mokdad, C. J. Murray, Popul. Health Metr. 11, 8 (2013). 13. J. S. Olshansky et al., Health Aff. 31, 1803-1813 (2011). 14. E. R. Meara, S. Richards, D. M. Cutler, Health Aff. 27, 350-360 (2008). 15. D. M. Cutler, F. Lange, E. Meara, S. Richards-Shubik, C. J. Ruhm, J. Health Econ. 30, 1174-1187 (2011). 16. J. K. Montez, L. F. Berkman, Am. J. Public Health 104, e82-e90 (2014). 17. Human Mortality Database; www.mortality.org. 18. D. D. Reidpath, P. Allotey, J. Epidemiol. Community Health 57, 344-346 (2003). 19. A. Case, A. Deaton, Proc. Natl. Acad. Sci. U.S.A. 112, 15078-15083 (2015). 20. J. Bound, A. Geronimus, J. Rodriguez, T. Waidman, "The Implications of Differential Trends in Mortality for Social Security Policy," University of Michigan Retirement Research Center Working Paper 2014-314 (2014). 21. J. B. Dowd, A. Hamoudi, Int. J. Epidemiol. 43, 983-988 (2014). 22. T. Goldring, F. Lange, S. Richards-Shubik, "Testing for Changes in the SES-Mortality Gradient When the Distribution of Education Changes Too," National Bureau of Economic Research Working Paper 20993 (2015). 23. A. S. Hendi, Int. J. Epidemiol. 44, 946-955 (2015). 24. A. Aizer, J. Currie, Science 344, 856-861 (2014). 25. D. Brown, A. Kowalski, I. Lurie, "Medicaid as an Investment in Children: What Is the Long-Term Impact on Tax Receipts?" National Bureau of Economic Research Working Paper 20835 (2015). 26. S. Cahodes, S. Kleiner, M. F. Lovenhem, M. Grossman, "Effect of Child Health Insurance Access on Schooling." National Bureau of Economic Research Working Paper 20178 (2014). 27. S. Miller, L. R. Wherry, "The Long-Term Health Effects of Early Life Medicaid Coverage," Social Science Research Network Working Paper 2466691 (2015). 28. L. R. Wherry, B. Meyer, "Saving Teens: Using and Eligibility Discontinuity to Estimate the Effects of Medicaid Eligibility." National Bureau of Economic Research Working Paper 18309 (2013). 29. L. R. Wherry, S. Miller, R. Kaestner, B. D. Meyer, "Childhood Medicaid Coverage and Later Life Health Care Utilization," National Bureau of Economic Research Working Paper 20929 (2015). 30. J. Ludwig, D. L. Miller, Q. J. Econ. 122, 159-208 (2007). 31. H. Hoynes, D. Whitmore-Schanzanbach, D. Almond, "Long Run Impacts of Childhood Access to the Safety Net," National Bureau of Economic Research Working Paper 18535 (2012). 32. A. Isen, M. Rossin-Slater, R. Walker, "Every Breath You Take Every Dollar You'll Make: The Long-Term Consequences of the Clean Air Act of 1970," National Bureau of Economic Research Working Paper 19858 (2014). 33. A. Fenelon, S. H. Preston, Demography 49, 797-818 (2012). 34. D. de Walque, J. Hum. Resour. 45, 682-717 (2010). 35. C. E. Finch, E. M. Crimmins, Science 305, 1736-1739 (2004). ACKNOWLEDGMENTS We thank M. Barbieri, A. Case, A. Deaton, J. Goldstein, I. Kuziemko, R. Lee, and K. Wachter, as well as seminar participants at Berkeley, the Chicago Federal Reserve, Fundação Getúlio Vargas São Paulo, Bonn University, University of Munich, Princeton University, ETH Zurich, and the University of Zurich for comments. Supported by Princeton Center for Translational Research on Aging grant 2P30AG024928. Data and code are available at http://dx.doi.org/10.7910/DVN/C2VYNM. SUPPLEMENTARY MATERIALS www.sciencemag.org/content/352/6286/708/suppl/DC1 Materials and Methods Figs. S1 to S8 Tables S1 to S4 References (36-38) 22 December 2015; accepted 17 March 2016 Published online 21 April 2016 10.1126/science.aaf1437 NEURODEVELOPMENT Complement and microglia mediate early synapse loss in Alzheimer mouse models Soyon Hong,1 Victoria F. Beja-Glasser,1* Bianca M. Nfonoyim,1* Arnaud Frouin,1 Shaomin Li,2 Saranya Ramakrishnan,1 Katherine M. Merry,1 Qiaogiao Shi,2 Arnon Rosenthal, 3,4,5 Ben A. Barres,6 Cynthia A. Lemere,2 Dennis J. Selkoe,2,7 Beth Stevens1,8+ Synapse loss in Alzheimer's disease (AD) correlates with cognitive decline. Involvement of microglia and complement in AD has been attributed to neuroinflammation, prominent late in disease. Here we show in mouse models that complement and microglia mediate synaptic loss early in AD. C1q, the initiating protein of the classical complement cascade, is increased and associated with synapses before overt plaque deposition. Inhibition of C1q, C3, or the microglial complement receptor CR3 reduces the number of phagocytic microglia, as well as the extent of early synapse loss. C1q is necessary for the toxic effects of soluble ß-amyloid (AB) oligomers on synapses and hippocampal long-term potentiation. Finally, microglia in adult brains engulf synaptic material in a CR3-dependent process when exposed to soluble Aß oligomers. Together, these findings suggest that the complement-dependent pathway and microglia that prune excess synapses in development are inappropriately activated and mediate synapse loss in AD. Downloaded from http://science.sciencemag.org/ on January 2, 2020 G enome-wide association studies impli- cate microglia and complement-related pathways in Alzheimer's disease (AD) (1). Previous research has demonstrated both beneficial and detrimental roles of com- plement and microglia in plaque-related neuro- pathology (2, 3); however, their roles in synapse loss, a major pathological correlate of cognitive decline in AD (4), remain to be identified. Emerg- ing research implicates microglia and immune- related mechanisms in brain wiring in the healthy 1F.M. Kirby Neurobiology Center, Boston Children's Hospital (BCH) and Harvard Medical School (HMS), Boston, MA 02115, USA. 2Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital (BWH) and HMS, Boston, MA 02115, USA. 3Alector Inc., 953 Indiana Street, San Francisco, CA 94107, USA. 4Annexon Biosciences, 280 Utah Avenue Suite 110, South San Francisco, CA 94080, USA. 5Department of Anatomy, University of California San Francisco (UCSF), San Francisco, CA 94143, USA. Department of Neurobiology, Stanford University School of Medicine, Palo Alto, CA 94305, USA. 7Prothena Biosciences, Dublin, Ireland. "Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. *These authors contributed equally to this work. +Corresponding author. Email: beth.stevens@childrens.harvard.edu brain (1). During development, C1q and C3 local- ize to synapses and mediate synapse elimination by phagocytic microglia (5-7). We hypothesized that this normal developmental synaptic pruning pathway is activated early in the AD brain and mediates synapse loss. The degree of region-specific synapse loss is a stronger correlate of cognitive decline in AD than counts of plaques, tangles, and neuronal loss (8, 9). To determine how early synapse loss occurs, we used superresolution structured illu- mination microscopy (SIM) (10) to quantify syn- apse density in hippocampal CA1 stratum radiatum of familial AD-mutant human amyloid precursor protein (hAPP) ("J20") transgenic mice (11). Quan- tification of colocalized pre- and postsynaptic puncta [synaptophysin and postsynaptic den- sity 95 (PSD95) (Fig. 1A); synaptotagmin and homer (fig. S1, A to D)] revealed a significant loss of synapses in J20 hippocampus at 3 to 4 months old (mo), an age that precedes plaque deposition (11, 12). Synapse loss in preplaque J20 CA1 was confirmed by electron microscopy (fig. S1G). Con- focal imaging also showed synapse loss in CA1, CA3, and dentate gyrus of 3 mo J20 hippo- campus but not in striatum (fig. S1E). Synapse 712 6 MAY 2016 . VOL 352 ISSUE 6286 sciencemag.org SCIENCE RESEARCH | REPORTS A WT J20 Synaptophysin + PSD95 Physin PSD95 150 Individual or Colocalized Puncta *** *** WT 100- % WT 50- 0 J20 B DG FC STR CRB Physin PSD95 Physin+PSD95 C1q DAP 150- *** WT WT J20 *** 100- 50- ns J20 C1q Intensity Levels (A.U.) T ns T 0 DG FC STR CRB C C1q PSD95 D C1g PSD95 % PSD95 Colocalized with C1q 250- * 200- Downloaded from http://science.sciencemag.org/ on January 2, 2020 % WT 150- 100- 50 0 WT J20 E WT J20 WT J20 Aß DAPI C1g Aß Levels in J20 DG C1q Levels in J20 DG 150- 150- * Veh % Vehicle-Rx ** 100- T % Vehicle-Rx $ 100- 50 50- CpdE 0 0 Veh CpdE Veh CpdE Fig. 1. C1q up-regulation and deposition onto synapses precede pre- plaque synapse loss in J20 mice. (A) Superresolution SIM images of synaptophysin (green)- and PSD95 (red)-immunoreactive puncta in stratum radiatum of 3 mo J20 or WT hippocampus (CA1). Quantification of synaptic puncta or their apposition using Imaris indicates selective loss of PSD95 in J20 hippocampus as compared to their WT littermate controls. See fig. S1. (B) Region-specific up-regulation of C1q (green) in 1 mo J20; DG, dentate gyrus; FC, frontal cortex; STR, striatum; CRB, cerebellum; DAPI, 4',6-diamidino-2-phenylindole. See fig. S2. (C) Orthogonal view of SIM image showing colocalization of C1q (green) and PSD95 (red). (D) Higher percentage of PSD95 colocalized with C1q in 1 mo J20 dentate gyrus versus WT. (E) Compound E reduces deposited soluble Aß (red) and Clq (green) in 1 mo J20 dentate gyrus, with minimal effect on C1q levels in WTmice. Scale bar, 2 um (A, C, and D) or 10 um (B and E). Means ± SEM; n = 3 or 4 mice per genotype or per treatment group per genotype. * P < 0.05, ** P < 0.01, or *** P < 0.001 using two-way analysis of variance (ANOVA) followed by Bonferroni posttest (A and B), two-tailed one-sample t test (D), or two- tailed unpaired t test (E). levels were not altered in 1 mo J20 brains ver- sus wild-type (WT) littermates (fig. S1F), sug- gesting that the hippocampal synaptic loss at 3 mo is likely not a result of abnormal synaptic development. We asked whether the classical complement cascade is up-regulated in preplaque brains when synapses are already vulnerable. C1q immuno- reactivity (13) (antibody now available at Abcam) was elevated in J20 brains as early as 1 mo and preceding synapse loss (Fig. 1B and fig. S1). C1q elevation was region-specific, particularly in the hippocampus and frontal cortex, two regions vulnerable to synapse loss (14) (Fig. 1B and fig. S2A). C1q immunoreactivity was comparable be- tween J20 and WT mice at postnatal day 21 (P21) (fig. S2B), suggesting that elevated levels at 1 mo are likely not a developmental artifact. C1q was also similarly increased in the hippocampus of another model of AD, the APP/PS1 (presenilin 1) mice (15) (fig. S2C). Notably, SIM demonstrated colocalization of C1q with PSD95-positive puncta in 1 mo J20 hippocampus (Fig. 1C). A higher percentage of PSD95 colocalized with C1q in the hippocampus of J20 mice than in that of WT littermates (Fig. 1D and fig. S3), suggesting that the C1q-associated synapses may be marked for elimination. Punctate Aß was found deposited in J20 hip- pocampus at 1 mo (fig. S4), long before Aß plaques deposit (11, 12), raising the question of whether C1q increase in these preplaque brains is dependent on soluble Aß levels. To test this hypothesis, we injected the mice with compound E, a y-secretase inhibitor that rapidly decreases Aß production (12). Compound E markedly re- duced soluble Aß levels in J20 mice; there was a corresponding reduction of C1q deposition (Fig. 1E), suggesting that Aß up-regulates Clq. SCIENCE sciencemag.org 6 MAY 2016 . VOL 352 ISSUE 6286 713 RESEARCH | REPORTS A Control Aß Monomer Aß Oligomer B C1q PSD95 % PSD95 Colocalized With C1q C1q 250- * 200- % WT 150- 100- 50- 0 Mon. Rx Olig. Rx C Control Aß Monomer Aß Oligomer % CD68 Occupancy in C1qa WT Microglia CD68 60- ** Iba1 % Total Microglia ## Control Rx AB Monomer Rx 40- AB Oligomer Rx T 20- 0 0 1 2 3 100- I D % Total Microglia % CD68 Occupancy in C1qa KO Microglia Control Rx 80- JAB Monomer Rx 60- Aß Oligomer Rx Downloaded from http://science.sciencemag.org/ on January 2, 2020 Fig. 3. Complement is necessary for synapse loss and dysfunction in AD models. (A) Aß oli- gomers induced loss of colocalized synapsin- and PSD95-immunoreactive puncta in the contralateral hippocampus of 3 mo WT mice (left panel); however, they failed to do so in C1qa KO mice (right panel). (B) Coinjection of Aß oligomers with the function- blocking antibody against C1q, ANX-M1, but not with its IgG isotype control, prevented synapse loss in WT mice. (C) Pretreatment of hippocampal slices with the anti-Clq antibody, ANX-M1, prevented Aß- mediated LTP inhibition (green) versus IgG (red). IgG alone had a minimal effect (blue) versus artificial cerebrospinal fluid (aCSF) vehicle (black). n = 6 to 11 slices per group. (D) Percentage of PSD95 co- localized with C3 is increased in APP/PS1 hippo- campus versus that of WT mice. (E and F) Genetic deletion of C3 prevents synapse loss in 4 mo APP/ PS1 mice. Quantification of colocalized immuno- reactive puncta for synaptotagmin and homer in dentate gyrus (E) or synaptophysin and PSD95 in CA1 stratum radiatum (F) of WT, APP/PS1, APP/ PS1xC3 KO, and C3 KO hippocampi. Means ± SEM; n = 3 to 5 mice per genotype or per treatment group per genotype. * P < 0.05, ** P < 0.01, or *** P < 0.001 using two-tailed one-sample t test (D), one- way (A, C, E, F) or two-way (B) ANOVA followed by Bonferroni posttest. ns, not significant. A Synapsin + PSD95 Colocalized Puncta 40- 20- 2 NOCH 1 0 0 1 T 3 %CD68 within Iba1+ Microglia Fig. 2. Oligomeric AB increases Clq and microglial phagocytic activity. (A and B) Soluble AB oligomers in WT mice led to elevation of Clq (green) (A) and a higher percentage of PSD95 (red) colocalization with Clq versus monomers (B). (C and D) oAß induced high levels of CD68 (green) immunoreactivity in Ibal-positive (red) microglia in WTmice (C), but not in those of C1qa KO mice (D). Both had negligible changes in morphology. See fig. S10. Scale bar, 10 um (A), 5 um (B), or 20 um (C). Means ± SEM; n = 3 to 5 mice per treatment group per genotype. * P < 0.05 using two-tailed t test (B) or *P < 0.05, ** P < 0.01 versus control-treated or ##P < 0.01 versus Aß monomer-treated using two-way ANOVA followed by Bonferroni posttest (C). B Synapsin + PSD95 Colocalized Puncta in WT Mice 400- 200- 200- ns Control ns lgG Ctrl % Control 150- 150- Aß Mon. % Control Aß Olig. % oAB-Rx 300- C1q Ab * ** 100- 100- T 200- 50- 50- 100- 0 0 0 C1qa WT C1qa KO PBS oAB PBS OAB C 250- aCSF +- IgG+aCSF % Baseline at 55 min of LTP Induction -IgG+oAB fEPSP slope (%) 200 C1qAb+oAB 200- LTP Magnitudes 180- ns *** 150 *** 160- 140- 100- 120- HFS 50- 100- -20 0 20 40 60 aCSF lgG+ lgG C1q Ab Time (min) aCSF +0Aß +0Aß D % PSD95 Coloc. With C3 Synaptotagmin + Homer in DG F Synaptophysin + PSD95 in CA1 250- * 150 ns 150 ns ns 200- ns * * % WT 150- % WT 100- % WT 100- 100- 50- 50- 50- O 0 WT APP/PS1 WT APP/PS1 APP/PS1 C3 KO WT APP/PS1APP/PS1 C3 KO 0 xC3 KO xC3 KO 714 6 MAY 2016 . VOL 352 ISSUE 6286 sciencemag.org SCIENCE RESEARCH | REPORTS A B Homer-GFP Homer-GFP Iba1 Iba1 15- % Engulfment *** 10- 5. 0- Aß Mon. AB Olig. C % Homer-GFP Engulfment by Microglia D Synaptotagmin + Homer Colocalized Puncta 250- 250- PBS 150- 150- PBS * 200- 200- oAB ns OAß % PBS-Rx % PBS-Rx % PBS-Rx ** % PBS-Rx 150- 150- 100- 100- ns 100- 100- 50- 50- 50- 50- 0 0 Homer-GFP Homer-GFP r0 0- xCR3 WT xCR3 KO To further address whether the increase of C1q is dependent on soluble Aß, and if so, which species, we injected soluble Aß oligomers or monomers into lateral ventricles of WT mice. Hippocampus contralateral to the injection site was examined to avoid any surgery-related ef- fects. Oligomeric Aß (oAß), which is prefibrillar in nature and acts as a mediator of synapse loss and dysfunction in AD (4), but not the relatively innocuous monomeric Aß or vehicle, induced C1q deposition (Fig. 2A and fig. S5). A higher percentage of PSD95 colocalized with C1q in oAß-injected versus monomer-injected mice (Fig. 2B), in a manner similar to this colocalization in J20 mice. Together, these findings show an early and aberrant increase and synaptic localization of C1q in multiple AD model systems. Further- more, fluorescent in situ hybridization (FISH) demonstrated up-regulated C1qa expression in microglia (fig. S6), implicating microglia as a major source of C1q in these preplaque brains. To test whether Clq and oAß act in a common pathway to eliminate synapses, we injected oAß into lateral ventricles of C1qa knockout (KO) mice (16). Soluble oAß induced a significant loss of co- localized synapsin- and PSD95-immunoreactive puncta in WT mice within 72 hours (Fig. 3A, left panel) (17). In contrast, oAß failed to induce syn- apse loss in C1qa KO mice (Fig. 3A, right panel), suggesting that Clq is required for oAß-induced synapse loss in vivo. To determine whether local, acute inhibition of C1 activation could similarly blunt the synaptotoxic effects of oAß, we used an antibody against C1q (anti-C1q) (ANX-M1, Annexon Biosciences), which blocks the classical complement cascade (see fig. S7 and supplemen- tary methods). Coadministration of the ANX-M1 anti-C1q antibody, but not its immunoglobulin G (IgG) isotype control, prevented oAß from inducing synapse loss in WT mice (Fig. 3B). Thus, block- ing C1 activation by either genetic or antibody- mediated means lessened oAB's synaptotoxic effects. To determine whether C1q is associated with synaptic dysfunction, we asked whether the established ability of oAß to potently inhibit long-term potentiation (LTP) (4) was depen- dent on C1q. We tested the functional effects of the ANX-M1 anti-C1q antibody in acute hippo- campal slices treated with oAB. IgG alone had negligible effects on LTP induction in WT mouse hippocampal slices and on the ability of oAß to inhibit LTP; however, pretreatment of hippo- campal slices with the anti-C1q antibody signif- icantly prevented the impairment of LTP by oAß (Fig. 3C). Neither ANX-M1 nor its IgG control altered basal synaptic neurotransmission (fig. S8). Collectively, these results in hippocampal slices and in mice support C1q as a key mediator of oAß-induced synaptic loss and dysfunction. In the healthy developing brain, C1q promotes activation of C3, which opsonizes subsets of synapses for elimination, a process that is down- regulated in the mature brain (5, 6). However, oAß induced a significant C3 deposition in WT adult mice (fig. S7A, upper panel). This was sig- nificantly reduced in both the C1qa KO (fig. S7A, lower panel) and the ANX-M1 anti-Clq antibody- treated WT mice (fig. S7B), suggesting that the C3 deposition in this model is downstream of the classical complement cascade. Consistent with these findings, a higher percentage of PSD95 colocalized with C3 in J20 and APP/PS1 brains (Fig. 3D and fig. S9). To determine whether C3 is necessary for early synapse loss in AD genetic models, we crossed APP/PS1 mice, which, simi- lar to the J20 mice, had a significant increase and localization of C1q and C3 onto hippocampal synapses (figs. S2C and S9), to C3-deficient mice (18). Quantification of colocalized pre- and post- synaptic puncta demonstrated synapse loss in 4 mo APP/PS1 hippocampus as compared to WT; however, APP/PS1xC3 KO mice did not display this synapse loss (Fig. 3, E and F). Together, our data indicate that genetic deletion of C3 amelio- rates synapse loss in APP/PS1 mice, providing further evidence that the classical complement cascade mediates early synapse loss in AD mouse models. Downloaded from http://science.sciencemag.org/ on January 2, 2020 CR3 WT CR3 KO Fig. 4. Microglia engulf synapses via CR3 upon oligomeric Aß challenge. (A) Orthogonal view of high-resolution confocal image shows colocalization of homer-GFP and Iba1 (red). (B) Three-dimensional reconstruction and surface rendering using Imaris demonstrate larger volumes of homer-GFP puncta inside microglia of oAß-injected contralateral hippo- campus versus those of monomer-injected. (C) Mi- croglia of homer-GFPxCR3 KO mice (right panel) show less engulfment of homer-GFP when chal- lenged with oAB versus those of homer-GFP mice (left panel). (D) Aß oligomers failed to induce syn- apse loss in the contralateral hippocampus of CR3 KO mice (right panel) as they did in WT mice (left panel). Scale bar, 5 um (A and B). Means ± SEM; n = 3 mice per treatment group per genotype (n = 6 to 17 microglia analyzed per mouse). * P < 0.05, ** P < 0.01, or *** P < 0.0001 using two-tailed t test (B) or two-tailed one-sample t test (C and D). ns, not significant. Microglia express complement receptors and mediate synaptic pruning in the developing brain (1, 6), raising the question of whether this normal developmental pruning pathway could be acti- vated to mediate synapse loss in the preplaque AD brain. Consistent with this hypothesis, mi- croglia had increased amounts of the lysosomal protein CD68 in J20 hippocampus compared to WT and less so in striatum, a less vulnerable region (figs. S1C and S10). Furthermore, in WT mice challenged with oAß, microglia had sig- nificantly increased levels of CD68 immuno- reactivity (Fig. 2C). However, in C1qa KO mice in which synapse loss was rescued, oAß failed to induce such an increase (Fig. 2D), suggesting that microglia eliminate synapses through the complement pathway. To directly test whether phagocytic microg- lia engulf synaptic elements, we adapted our in vivo synaptic engulfment assay (19) using in- tracerebroventricular injections of Aß in homer- GFP (green fluorescent protein) mice (20) (Fig. 4.A). oAß induced a significantly higher volume of internalized homer-GFP in microglia than monomeric Aß controls did at the contralateral hippocampus (Fig. 4B), indicating that microglia engulf synaptic elements when challenged with oAB. Internalized homer-GFP often colocalized SCIENCE sciencemag.org 6 MAY 2016 . VOL 352 ISSUE 6286 715 RESEARCH | REPORTS with CD68 (fig. S11A), suggesting that the en- gulfed synapses are internalized into lysosom- al compartments in a manner similar to that of developmental synaptic pruning (6). Notab- ly, oAß failed to increase synaptic engulfment in microglia lacking CR3 (21), a high-affinity receptor for C3 expressed on macrophages [homer-GFPxCR3 KO versus homer-GFP mice, which received tail vein injections of phosphate- buffered saline (PBS) or oAß (Fig. 4C)]. These data demonstrate that CR3 is necessary for oAß-dependent engulfment of synapses by microglia. To test whether inhibition in microglial en- gulfment leads to protection against oAß-induced synapse loss, we performed tail vein injections of oAB into WT and CR3 KO mice. oAß induced synapse loss in the hippocampus of WT mice but not in that of CR3 KO mice (Fig. 4D). All CR3-positive microglia were P2RY12-positive (fig. S11), indicating that they are resident cells (22). Altogether, these results suggest that resi- dent microglia engulf synaptic material when chal- lenged by oAß through a complement-dependent mechanism. Synaptic deficits occur in early AD and mild cognitive impairment before onset of plaques and are some of the first signs of the neuronal de- generative process (4, 23-25). Here we identify critical synaptotoxic roles of complement and microglia in AD models before plaque forma- tion and neuroinflammation, in regions of the hippocampus undergoing synapse loss. Using multiple experimental approaches, we demon- strate a region-specific increase of phagocytic microglia and accumulation of C1q and C3 on synapses in preplaque brains. Microglia in the adult brain, when challenged with synapto- toxic, soluble Aß oligomers, engulf synapses in the absence of plaque aggregates; deletion of CR3 blocks this process. Finally, inhibiting C1q, C3, or CR3 activity rescues synaptic loss and dysfunction. Our data suggest a local activation of a de- velopmental pruning pathway (5, 6) as a key mechanism underlying oAß-induced synapse loss in preplaque AD brain. C1q is aberrantly increased by diffusible oAß in a region-specific manner and deposits onto synapses, triggering the activation of downstream classical comple- ment pathway and phagocytic microglia. Block- ing Aß production in J20 mice significantly ameliorated C1q deposition in the hippocampus, and genetic or antibody-mediated inhibition of complement blocks oAß from inducing microg- lial synaptic engulfment, synapse loss, and LTP inhibition. These complementary findings have direct therapeutic relevance. We propose a model in which Clq and oAß operate in a common pathway to activate the complement cascade and drive synapse elimi- nation by microglia through CR3 (fig. S12). This could occur in multiple ways: Soluble oAß asso- ciates with synaptic membranes and other syn- aptic markers (4, 26); thus, oAß bound to synapses may anchor Clq directly. Alternatively, oAß binding to synapses may weaken the synapse (4) and expose a C1q receptor. Although spe- cific receptors for C1q at synapses are not yet known, we have shown that C1q binds syn- apses in vulnerable regions undergoing syn- apse loss (5, 27). It is also plausible that oAß and C1q may work indirectly to mediate syn- apse loss through cytokines such as trans- forming growth factor-ß (7), through microglial or astrocytic activation, or through other mech- anisms, including major histocompatibility complex class I (MHCI)-PirB, another immune pathway critical for synapse elimination in de- velopment and AD (28-30). Finally, our studies show that resident mi- croglia in the adult central nervous system phagocytose synapses when challenged by syn- aptotoxic oAß, implicating microglia as poten- tial cellular mediators of synapse loss. Although microglia and complement activation are pro- minently involved in plaque maintenance and related periplaque neuropathology, their roles have heretofore been largely regarded as a sec- ondary event related to neuroinflammation (2). Our studies directly challenge this view and sug- gest that microglia and immune-related path- ways can act as early mediators of synapse loss and dysfunction that occur in AD models be- fore plaques form. Although the complement pathway may not be involved in all patholog- ical routes to AD, including plaque-associated synapse loss, the work reported here provides new insights into how synapses are lost in AD. It will be important in future studies to examine whether this microglia or the complement- dependent pathway also plays a role in plaque- associated synapse loss or in other synaptopathies, including tauopathies and Huntington's dis- ease. If so, our findings may suggest comple- ment and microglia as potential early therapeutic targets in AD and other neurodegenerative dis- eases involving synaptic dysfunction and memory decline. REFERENCES AND NOTES 1. S. Hong, L. Dissing-Olesen, B. Stevens, Curr. Opin. Neurobiol. 36, 128-134 (2016). 2. T. Wyss-Coray, J. Rogers, Cold Spring Harb. Perspect. Med. 2, a006346 (2012). 3. M. E. Benoit et al., J. Biol. Chem. 288, 654-665 (2013). 4. L. Mucke, D. J. Selkoe, Cold Spring Harb. Perspect. Med. 2, a006338 (2012). 5. B. Stevens et al., Cell 131, 1164-1178 (2007). 6. D. P. Schafer et al., Neuron 74, 691-705 (2012). 7. A. R. Bialas, B. Stevens, Nat. Neurosci. 16, 1773-1782 (2013). 8. S. T. DeKosky, S. W. Scheff, Ann. Neurol. 27, 457-464 (1990). 9. R. D. Terry et al., Ann. Neurol. 30, 572-580 (1991). 10. S. Hong, D. Wilton, B. Stevens, D. S. Richardson, Structured Illumination Microscopy for the investigation of synaptic structure and function. Methods in Molecular Biology; Synapse Development: Methods and Protocols. 11. L. Mucke et al., J. Neurosci. 20, 4050-4058 (2000). 12. S. Hong et al., J. Neurosci. 31, 15861-15869 (2011). 13. A. H. Stephan et al., J. Neurosci. 33, 13460-13474 (2013). 14. J. A. Harris et al., J. Neurosci. 30, 372-381 (2010). 15. J. L. Jankowsky et al., Hum. Mol. Genet. 13, 159-170 (2004). 16. M. Botto et al., Nat. Genet. 19, 56-59 (1998). 17. D. B. Freir et al., Neurobiol. Aging 32, 2211-2218 (2011). 18. M. R. Wessels et al., Proc. Natl. Acad. Sci. U.S.A. 92, 11490-11494 (1995). 19. D. P. Schafer, E. K. Lehrman, C. T. Heller, B. Stevens, J. Vis. Exp. 88, 51482 (2014). 20. T. Ebihara, I. Kawabata, S. Usui, K. Sobue, S. Okabe, J. Neurosci. 23, 2170-2181 (2003). 21. A. Coxon et al., Immunity 5, 653-666 (1996). 22. O. Butovsky et al., Nat. Neurosci. 17, 131-143 (2014). 23. D. J. Selkoe, Science 298, 789-791 (2002). 24. S. W. Scheff, D. A. Price, F. A. Schmitt, E. J. Mufson, Neurobiol. Aging 27, 1372-1384 (2006). 25. S. W. Scheff, D. A. Price, F. A. Schmitt, S. T. DeKosky, E. J. Mufson, Neurology 68, 1501-1508 (2007). 26. S. Hong et al., Neuron 82, 308-319 (2014). 27. A. H. Stephan, B. A. Barres, B. Stevens, Annu. Rev. Neurosci. 35, 369-389 (2012). 28. A. Datwani et al., Neuron 64, 463-470 (2009). 29. T. Kim et al., Science 341, 1399-1404 (2013). 30. H. Lee et al., Nature 509, 195-200 (2014). ACKNOWLEDGMENTS We thank B. Sabatini (HMS), T. Bartels (BWH), and members of the Stevens laboratory for critical reading of the manuscript; L. Dissing-Olesen (BCH) for help with the conceptual figure (fig. S12), M. Ericsson [HMS electron microscopy (EM) facility] for EM imaging, K. Kapur (BCH) for advice on statistics, D. M. Walsh (BWH) for Aß oligomers (S26C), S. Okabe (University of Tokyo) for homer-GFP mice, and M. Leviten and T. Yednock (Annexon Biosciences) for characterization and advice on the ANX-M1 anti-C1q antibody; D. Richardson (Harvard Center for Biological Imaging), A. Hill BCH Intellectual and Developmental Disabilities Research Center Cellular Imaging Core NIH-P30-HD-18655, and H. Elliot and T. Xie (HMS Image and Data Analysis Core) for assistance with imaging and data analysis; and S. Kim (BWH), K. Colodner (BCH), and S. Matousek (BWH) for assistance with mice. The J20 mice, C1qa KO mice, P2RY12 antibody, and the ANX-M1 C1q function-blocking antibody are available from L. Mucke, M. Botto, O. Butovsky, and A. Rosenthal under material transfer agreements with UCSF Gladstone, Imperial College London, BWH, and Annexon Biosciences, respectively. A.R. is a cofounder, consultant, and chairman of the board of directors; B.A.B. is a cofounder and chairman of the scientific advisory board; and B.S. serves on the scientific advisory board of Annexon LLC. A.R., B.A.B., and B.S. are minor shareholders of Annexon LLC. All other authors declare no competing financial interests related to this project. The following patents related to this project have been granted or applied for: PCT/2015/010288 (S.H. and B.S.), US14/988387 and EP14822330 (S.H., A.R., and B.S.), and US8148330, US9149444, US20150368324, US20150368325, US20150368326, and US20120328601 (B.S. and B.A.B.). This work was funded by an Edward R. and Anne G. Lefler Fellowship (S.H.), Coins for Alzheimer's Research Trust (B.S.), Fidelity Biosciences Research Initiative (F-Prime) (B.S. and C.A.L.), JPB Foundation (B.A.B.), the National Institutes of Health AG000222 (S.H.), National Institute of Neurological Disorders and Stroke-NIH R01NS083845 (D.J.S.), National Institute on Aging-NIH 1RF1AG051496A (B.S.). Supplementary materials contain additional data, including materials and methods. S.H. and B.S. designed the study and wrote the manuscript, with help from all authors. S.H. performed most experiments and data analysis; V.F.B .- G. and B.M.N. performed microglial activation and engulfment experiments along with immunohistochemistry; S.R. and K.M.M. performed C1q immunohistochemistry; A.F. performed FISH; S.L. performed electrophysiology; Q.S. and C.A.L. assisted with design and collection of APP/PS1 tissue; A.R. and B.A.B. designed and characterized the ANX-M1 anti-C1q antibody; and D.J.S. contributed in the discussions and experimental design. SUPPLEMENTARY MATERIALS www.sciencemag.org/content/352/6286/712/suppl/DC1 Materials and Methods Figs. S1 to S12 10 November 2015; accepted 18 March 2016 Published online 31 March 2016 10.1126/science.aad8373 716 6 MAY 2016 . VOL 352 ISSUE 6286 sciencemag.org SCIENCE Downloaded from http://science.sciencemag.org/ on January 2, 2020 Science Complement and microglia mediate early synapse loss in Alzheimer mouse models Soyon Hong, Victoria F. Beja-Glasser, Bianca M. Nfonoyim, Arnaud Frouin, Shaomin Li, Saranya Ramakrishnan, Katherine M. Merry, Qiaoqiao Shi, Arnon Rosenthal, Ben A. Barres, Cynthia A. Lemere, Dennis J. Selkoe and Beth Stevens Science 352 (6286), 712-716. DOI: 10.1126/science.aad8373originally published online March 31, 2016 Too much cleaning up The complement system and microglia seek out and destroy unwanted cellular debris for the peripheral immune system as well as excess synapses in the developing brain. Hong et al. now show how the system may go haywire in adults early in the progression toward Alzheimer's disease (AD). Aberrant synapse loss is an early feature of Alzheimer's and correlates with cognitive decline. In mice susceptible to AD, complement was associated with synapses, and microglial function was required for synapse loss. The authors speculate that aberrant activation of this "trash disposal" system underlies AD pathology. Science, this issue p. 712 ARTICLE TOOLS http://science.sciencemag.org/content/352/6286/712 SUPPLEMENTARY MATERIALS http://science.sciencemag.org/content/suppl/2016/03/30/science.aad8373.DC1 http://stm.sciencemag.org/content/scitransmed/7/309/309ra164.full RELATED CONTENT http://stm.sciencemag.org/content/scitransmed/6/241/241cm5.full http://stm.sciencemag.org/content/scitransmed/6/226/226ra30.full http://stke.sciencemag.org/content/sigtrans/8/402/ec329.abstract http://stke.sciencemag.org/content/sigtrans/5/238/ra61.full http://stke.sciencemag.org/content/sigtrans/8/373/ec100.abstract http://stke.sciencemag.org/content/sigtrans/9/427/ra47.full http://stke.sciencemag.org/content/sigtrans/9/427/pc11.full http://stke.sciencemag.org/content/sigtrans/10/470/eaan1468.full http://stm.sciencemag.org/content/scitransmed/7/278/278ra33.full REFERENCES This article cites 29 articles, 11 of which you can access for free http://science.sciencemag.org/content/352/6286/712#BIBL PERMISSIONS http://www.sciencemag.org/help/reprints-and-permissions Use of this article is subject to the Terms of Service Science (print ISSN 0036-8075; online ISSN 1095-9203) is published by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. The title Science is a registered trademark of AAAS. Copyright @ 2016, American Association for the Advancement of Science Downloaded from http://science.sciencemag.org/ on January 2, 2020See Answer
- Q15:Question The nervous system sends electrical impulses that travel very rapidly and tend to result in short term effects, such as muscle contraction. The endocrine system is responsible for much slower signals that tend to have a much longer lasting effect on the body. a) Discuss how the endocrine system sends signals around the body, where the signals originate from, where they are targeted, and explain why these signals take much longer than nerve impulses. The major glands, hormones and target tissues can be included in a table which needs to be accompanied by a brief discussion of how the endocrine system works. b) Explain how the activity of these endocrine glands is regulated. Total word count: 450-500 words Instructions: Answer all the questions following the instructions. Strictly do not use AI for solving questions. The solution should be free of Plagiarism. Solution to be formatted in APA and use appropriate references with in-text citations in APA.See Answer
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