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3.3 Psychologists Study the Brain Using Many Different Methods

Learning objective.

  • Compare and contrast the techniques that scientists use to view and understand brain structures and functions.

One problem in understanding the brain is that it is difficult to get a good picture of what is going on inside it. But there are a variety of empirical methods that allow scientists to look at brains in action, and the number of possibilities has increased dramatically in recent years with the introduction of new neuroimaging techniques. In this section we will consider the various techniques that psychologists use to learn about the brain. Each of the different techniques has some advantages, and when we put them together, we begin to get a relatively good picture of how the brain functions and which brain structures control which activities.

Perhaps the most immediate approach to visualizing and understanding the structure of the brain is to directly analyze the brains of human cadavers. When Albert Einstein died in 1955, his brain was removed and stored for later analysis. Researcher Marian Diamond (1999) later analyzed a section of the Einstein’s cortex to investigate its characteristics. Diamond was interested in the role of glia, and she hypothesized that the ratio of glial cells to neurons was an important determinant of intelligence. To test this hypothesis, she compared the ratio of glia to neurons in Einstein’s brain with the ratio in the preserved brains of 11 other more “ordinary” men. However, Diamond was able to find support for only part of her research hypothesis. Although she found that Einstein’s brain had relatively more glia in all the areas that she studied than did the control group, the difference was only statistically significant in one of the areas she tested. Diamond admits a limitation in her study is that she had only one Einstein to compare with 11 ordinary men.

Lesions Provide a Picture of What Is Missing

An advantage of the cadaver approach is that the brains can be fully studied, but an obvious disadvantage is that the brains are no longer active. In other cases, however, we can study living brains. The brains of living human beings may be damaged, for instance, as a result of strokes, falls, automobile accidents, gunshots, or tumors. These damages are called lesions . In rare occasions, brain lesions may be created intentionally through surgery, such as that designed to remove brain tumors or (as in split-brain patients) to reduce the effects of epilepsy. Psychologists also sometimes intentionally create lesions in animals to study the effects on their behavior. In so doing, they hope to be able to draw inferences about the likely functions of human brains from the effects of the lesions in animals.

Lesions allow the scientist to observe any loss of brain function that may occur. For instance, when an individual suffers a stroke, a blood clot deprives part of the brain of oxygen, killing the neurons in the area and rendering that area unable to process information. In some cases, the result of the stroke is a specific lack of ability. For instance, if the stroke influences the occipital lobe, then vision may suffer, and if the stroke influences the areas associated with language or speech, these functions will suffer. In fact, our earliest understanding of the specific areas involved in speech and language were gained by studying patients who had experienced strokes.

Figure 3.13

Phineas Gages skull diagram

Areas in the frontal lobe of Phineas Gage were damaged when a metal rod blasted through it. Although Gage lived through the accident, his personality, emotions, and moral reasoning were influenced. The accident helped scientists understand the role of the frontal lobe in these processes.

It is now known that a good part of our moral reasoning abilities are located in the frontal lobe, and at least some of this understanding comes from lesion studies. For instance, consider the well-known case of Phineas Gage, a 25-year-old railroad worker who, as a result of an explosion, had an iron rod driven into his cheek and out through the top of his skull, causing major damage to his frontal lobe (Macmillan, 2000). Although remarkably Gage was able to return to work after the wounds healed, he no longer seemed to be the same person to those who knew him. The amiable, soft-spoken Gage had become irritable, rude, irresponsible, and dishonest. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the frontal lobe is involved in emotion and morality (Damasio et al., 2005).

More recent and more controlled research has also used patients with lesions to investigate the source of moral reasoning. Michael Koenigs and his colleagues (Koenigs et al., 2007) asked groups of normal persons, individuals with lesions in the frontal lobes, and individuals with lesions in other places in the brain to respond to scenarios that involved doing harm to a person, even though the harm ultimately saved the lives of other people (Miller, 2008).

In one of the scenarios the participants were asked if they would be willing to kill one person in order to prevent five other people from being killed. As you can see in Figure 3.14 “The Frontal Lobe and Moral Judgment” , they found that the individuals with lesions in the frontal lobe were significantly more likely to agree to do the harm than were individuals from the two other groups.

Figure 3.14 The Frontal Lobe and Moral Judgment

Control participants and participants with lesions in areas other than the frontal lobes have much lower engagements in harm and participants with lesions in the frontal lobes have engage much higher in harm.

Koenigs and his colleagues (2007) found that the frontal lobe is important in moral judgment. Persons with lesions in the frontal lobe were more likely to be willing to harm one person in order to save the lives of five others than were control participants or those with lesions in other parts of the brain.

Recording Electrical Activity in the Brain

In addition to lesion approaches, it is also possible to learn about the brain by studying the electrical activity created by the firing of its neurons. One approach, primarily used with animals, is to place detectors in the brain to study the responses of specific neurons. Research using these techniques has found, for instance, that there are specific neurons, known as feature detectors , in the visual cortex that detect movement, lines and edges, and even faces (Kanwisher, 2000).

Figure 3.15

A young man wearing an EEG

A participant in an EEG study has a number of electrodes placed around the head, which allows the researcher to study the activity of the person’s brain. The patterns of electrical activity vary depending on the participant’s current state (e.g., whether he or she is sleeping or awake) and on the tasks the person is engaging in.

A less invasive approach, and one that can be used on living humans, is electroencephalography (EEG) . The EEG is a technique that records the electrical activity produced by the brain’s neurons through the use of electrodes that are placed around the research participant’s head. An EEG can show if a person is asleep, awake, or anesthetized because the brain wave patterns are known to differ during each state. EEGs can also track the waves that are produced when a person is reading, writing, and speaking, and are useful for understanding brain abnormalities, such as epilepsy. A particular advantage of EEG is that the participant can move around while the recordings are being taken, which is useful when measuring brain activity in children who often have difficulty keeping still. Furthermore, by following electrical impulses across the surface of the brain, researchers can observe changes over very fast time periods.

Peeking Inside the Brain: Neuroimaging

Although the EEG can provide information about the general patterns of electrical activity within the brain, and although the EEG allows the researcher to see these changes quickly as they occur in real time, the electrodes must be placed on the surface of the skull and each electrode measures brain waves from large areas of the brain. As a result, EEGs do not provide a very clear picture of the structure of the brain.

But techniques exist to provide more specific brain images. Functional magnetic resonance imaging (fMRI) is a type of brain scan that uses a magnetic field to create images of brain activity in each brain area . The patient lies on a bed within a large cylindrical structure containing a very strong magnet. Neurons that are firing use more oxygen, and the need for oxygen increases blood flow to the area. The fMRI detects the amount of blood flow in each brain region, and thus is an indicator of neural activity.

Very clear and detailed pictures of brain structures (see, e.g., Figure 3.16 “fMRI Image” ) can be produced via fMRI. Often, the images take the form of cross-sectional “slices” that are obtained as the magnetic field is passed across the brain. The images of these slices are taken repeatedly and are superimposed on images of the brain structure itself to show how activity changes in different brain structures over time. When the research participant is asked to engage in tasks while in the scanner (e.g., by playing a game with another person), the images can show which parts of the brain are associated with which types of tasks. Another advantage of the fMRI is that is it noninvasive. The research participant simply enters the machine and the scans begin.

Although the scanners themselves are expensive, the advantages of fMRIs are substantial, and they are now available in many university and hospital settings. fMRI is now the most commonly used method of learning about brain structure.

Figure 3.16 fMRI Image

fMRI image

The fMRI creates brain images of brain structure and activity. In this image the red and yellow areas represent increased blood flow and thus increased activity. From your knowledge of brain structure, can you guess what this person is doing?

Photo courtesy of the National Institutes of Health, Wikimedia Commons – public domain.

There is still one more approach that is being more frequently implemented to understand brain function, and although it is new, it may turn out to be the most useful of all. Transcranial magnetic stimulation (TMS) is a procedure in which magnetic pulses are applied to the brain of living persons with the goal of temporarily and safely deactivating a small brain region . In TMS studies the research participant is first scanned in an fMRI machine to determine the exact location of the brain area to be tested. Then the electrical stimulation is provided to the brain before or while the participant is working on a cognitive task, and the effects of the stimulation on performance are assessed. If the participant’s ability to perform the task is influenced by the presence of the stimulation, then the researchers can conclude that this particular area of the brain is important to carrying out the task.

The primary advantage of TMS is that it allows the researcher to draw causal conclusions about the influence of brain structures on thoughts, feelings, and behaviors. When the TMS pulses are applied, the brain region becomes less active, and this deactivation is expected to influence the research participant’s responses. Current research has used TMS to study the brain areas responsible for emotion and cognition and their roles in how people perceive intention and approach moral reasoning (Kalbe et al., 2010; Van den Eynde et al., 2010; Young, Camprodon, Hauser, Pascual-Leone, & Saxe, 2010). TMS is also used as a treatment for a variety of psychological conditions, including migraine, Parkinson’s disease, and major depressive disorder.

Research Focus: Cyberostracism

Neuroimaging techniques have important implications for understanding our behavior, including our responses to those around us. Naomi Eisenberger and her colleagues (2003) tested the hypothesis that people who were excluded by others would report emotional distress and that images of their brains would show that they experienced pain in the same part of the brain where physical pain is normally experienced. In the experiment, 13 participants were each placed into an fMRI brain-imaging machine. The participants were told that they would be playing a computer “Cyberball” game with two other players who were also in fMRI machines (the two opponents did not actually exist, and their responses were controlled by the computer).

Each of the participants was measured under three different conditions. In the first part of the experiment, the participants were told that as a result of technical difficulties, the link to the other two scanners could not yet be made, and thus at first they could not engage in, but only watch, the game play. This allowed the researchers to take a baseline fMRI reading. Then, during a second inclusion scan, the participants played the game, supposedly with the two other players. During this time, the other players threw the ball to the participants. In the third, exclusion, scan, however, the participants initially received seven throws from the other two players but were then excluded from the game because the two players stopped throwing the ball to the participants for the remainder of the scan (45 throws).

The results of the analyses showed that activity in two areas of the frontal lobe was significantly greater during the exclusion scan than during the inclusion scan. Because these brain regions are known from prior research to be active for individuals who are experiencing physical pain, the authors concluded that these results show that the physiological brain responses associated with being socially excluded by others are similar to brain responses experienced upon physical injury.

Further research (Chen, Williams, Fitness, & Newton, 2008; Wesselmann, Bagg, & Williams, 2009) has documented that people react to being excluded in a variety of situations with a variety of emotions and behaviors. People who feel that they are excluded, or even those who observe other people being excluded, not only experience pain, but feel worse about themselves and their relationships with people more generally, and they may work harder to try to restore their connections with others.

Key Takeaways

  • Studying the brains of cadavers can lead to discoveries about brain structure, but these studies are limited due to the fact that the brain is no longer active.
  • Lesion studies are informative about the effects of lesions on different brain regions.
  • Electrophysiological recording may be used in animals to directly measure brain activity.
  • Measures of electrical activity in the brain, such as electroencephalography (EEG), are used to assess brain-wave patterns and activity.
  • Functional magnetic resonance imaging (fMRI) measures blood flow in the brain during different activities, providing information about the activity of neurons and thus the functions of brain regions.
  • Transcranial magnetic stimulation (TMS) is used to temporarily and safely deactivate a small brain region, with the goal of testing the causal effects of the deactivation on behavior.

Exercise and Critical Thinking

  • Consider the different ways that psychologists study the brain, and think of a psychological characteristic or behavior that could be studied using each of the different techniques.

Chen, Z., Williams, K. D., Fitness, J., & Newton, N. C. (2008). When hurt will not heal: Exploring the capacity to relive social and physical pain. Psychological Science, 19 (8), 789–795.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings (pp. 21–28). New York, NY: Psychology Press.

Diamond, M. C. (1999). Why Einstein’s brain? New Horizons for Learning . http://www.newhorizons.org/neuro/diamond_einstein.htm -->

Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302 (5643), 290–292.

Kalbe, E., Schlegel, M., Sack, A. T., Nowak, D. A., Dafotakis, M., Bangard, C.,…Kessler, J. (2010). Dissociating cognitive from affective theory of mind: A TMS study. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 46 (6), 769–780.

Kanwisher, N. (2000). Domain specificity in face perception. Nature Neuroscience, 3 (8), 759–763.

Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M., & Damasio, A. (2007). Damage to the prefontal cortex increases utilitarian moral judgments. Nature, 446 (7138), 908–911.

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Macmillan, M. (2000). An odd kind of fame: Stories of Phineas Gage . Cambridge, MA: MIT Press.

Miller, G. (2008). The roots of morality. Science, 320 , 734–737.

Van den Eynde, F., Claudino, A. M., Mogg, A., Horrell, L., Stahl, D.,…Schmidt, U. (2010). Repetitive transcranial magnetic stimulation reduces cue-induced food craving in bulimic disorders. Biological Psychiatry, 67 (8), 793–795.

Wesselmann, E. D., Bagg, D., & Williams, K. D. (2009). “I feel your pain”: The effects of observing ostracism on the ostracism detection system. Journal of Experimental Social Psychology, 45 (6), 1308–1311.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Neuroimaging: Visualizing Brain Structure and Function

Case studies.

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7.1. Case Study 1

7.2. case study 2.

Susan Shin, a 24-year-old healthy graduate student is crossing the street to attend class when a delivery truck runs a red light and hits her. She is thrown several feet, hits her head on the curb, and loses consciousness. EMTs have difficulty obtaining blood pressure and her oxygen saturation is below normal. In the Emergency Department (ED) she is still unconscious and is intubated. She is found to have multiple rib fractures, a collapsed lung, and is markedly hypotensive from internal bleeding. A non-contrast CT scan of her brain shows diffuse subarachnoid blood and contusions of her frontal and temporal lobes. Neck CT shows no fractures.

A chest tube is inserted, helping Susan's lung reinflate. She is attached to a ventilator. She is transfused and rushed to emergency surgery which normalizes her blood pressure. After surgery she enters the ICU. Forty hours later, well after the anesthesia was worn off, she still has not regained consciousness. A neurologist is called.

Imaging is appropriate at this point for diagnostic purposes. Further structural imaging can help identify the cause of Susan's coma. Although a repeat CT scan would probably also have been done to follow up on the blood in the brain, MRI will show more detail of which structures are injured. MRI of the spinal cord would be done to exclude a cord injury from the trauma.

The neurologist recommends MRIs of the brain and its vasculature and of the cervical spine. Overnight the ICU nurse notices some quick jerks of the fingers that could represent seizure, so the resident physician obtains an electroencephalogram (EEG). The study shows diffuse slowing of the brain's normal electrical activity as is often seen in comatose patients, but no evidence of seizures.

Susan does not have an Advance Directive in the form of a designated health care proxy or a living will. She also has no spouse and no children, so her parents are the next in line as her surrogates to make medical decisions for her. They say that she was a competitive athlete and active in her church and would want "to fight this out."

What it means to have positive outcome in this setting is not well defined overall, but one attempt is the Glasgow Outcome Scale, which defines "moderate disability" as independence in daily living with physical or mental limitations preventing return to one's previous level of function. For traumatic and non- traumatic coma, detailed tables based on studies with large sample sizes exist, correlating the different features seen on neurological exams with the percentages of patients who go on to recover neurological function to various extents (ranging from none to resumption of former activities). , These numbers can be cited to families who want to know overall odds and to prepare them for the possibility of severe disability if expected. However, except for some scenarios, the percentages cannot foretell the outcome for any one individual patient.

Susan has two different types of injury, both traumatic and anoxic/ischemic, making her prognosis more difficult and complex, because it is not clear which is more severe or contributing most to her current condition.

She is currently in a vegetative state (VS), "awake but unaware." It is too early to comment on its permanence. Data on the likelihood of recovery from the vegetative state collected by the Multi-Society Task Force described outcomes beginning from one month of ongoing VS, after which the term persistent may apply. This patient could remain vegetative or could go on to recover to a higher level of awareness such as minimal consciousness.

Although they are not required for diagnosis of a vegetative state, electrodiagnostic studies can sometimes aid in prognosis. EEG can exclude seizures or demonstrate other patterns known to be associated with poor outcome. Somatosensory evoked potentials (SSEPs) and Brainstem auditory- evoked responses (BAERs) can test the integrity of different circuits in the cortex and brainstem, respectively.

The parents want to know if Susan knows that they are there at her bedside, if she can hear them talking to her, and if she is in pain. The neurologist explains that it is not known how much of what healthy people would recognize as conscious awareness is present in minimally conscious individuals. It is probably not the case that she is living an active mental life inside her severely limited body, the way a person with a neuro-degenerative disease might.

The neurologist further explains that patients who recover from MCS do not recall the period of minimal consciousness. Rather, it is thought, and imaging has supported the idea, that MCS involves a fluctuating limited ability to interact, and that these patients have limited activation of selected areas of cortex permitting some interaction without the full integration required for complete awareness. What exactly is intact is highly individual and dependent on the injury each patient sustains. Large areas of pain networks may be preserved, so it is reasonable to ensure patients' comfort, including pain medication. Several studies have shows preservation of auditory networks and at least one has shown evidence of auditory processing and cognitive command following, so although it is unlikely that the patient has total awareness of her family's presence, her brain could be processing their speech rudimentarily.

The neurologist reassures the Shins that Susan will continue to be examined at regular intervals for evidence of neurological recovery. He also provides them with a realistic explanation of her likely severe degree of permanent disability.

The proposed neuroimaging studies will be experimental and descriptive. They are not validated for prognosis in Susan's case. Currently, there are many research studies but no large, validated set of prognostic data using fMRI or PET for patients in MCS, so even if it were performed, the test's results would be of uncertain significance. The results might enter a database which in aggregate data could be used to prospectively or retrospectively correlate eventual outcome with features seen on such imaging, and thus might eventually help scientists form prognostic schemes such as those currently in existence for coma. The benefit will not be for this patient or family, but for others in the future. Eventually, physicians may be able to construct a functional, neuroimaging profile of a particular injured patient that gives good information about likely recovery. However, that is a future direction, not a current reality.

Stanford University

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Stanford researchers observe decision making in the brain – and influence the outcomes

A team of neuroscientists and engineers have developed a system that can show the neural process of decision making in real time, including the mental process of flipping between options before expressing a final choice.

In the course of deciding whether to keep reading this article, you may change your mind several times. While your final choice will be obvious to an observer – you’ll continue to scroll and read, or you’ll click on another article – any internal deliberations you had along the way will most likely be inscrutable to anyone but you. That clandestine hesitation is the focus of research, published Jan. 20 in Nature , by Stanford University researchers who study how cognitive deliberations are reflected in neural activity.

case study activity parts of the brain

Stanford neuroscientists and engineers used neural implants to track decision making in the brain, in real time. (Image credit: Gil Costa)

These scientists and engineers developed a system that read and decoded the activity of monkeys’ brain cells while the animals were asked to identify whether an animation of moving dots was shifting slightly left or right. The system successfully revealed the monkeys’ ongoing decision-making process in real time, complete with the ebb and flow of indecision along the way.

“I was just looking at the decoded activity trace on the screen, not knowing which way the dots were moving or what the monkey was doing, and I could tell Sania [Fong], the lab manager, ‘He’s going to choose right,’ seconds before the monkey initiated the movement to report that same choice,” recalled Diogo Peixoto, a former postdoctoral scholar in neurobiology and co-lead author of the paper. “I would get it right 80 to 90 percent of the time, and that really cemented that this was working.”

In subsequent experiments, the researchers were even able to influence the monkeys’ final decisions through subliminal manipulations of the dot motion.

“Fundamentally, much of our cognition is due to ongoing neural activity that is not reflected overtly in behavior, so what’s exciting about this research is that we’ve shown that we can now identify and interpret some of these covert, internal neural states,” said study senior author William Newsome , the Harman Family Provostial Professor in the Department of Neurobiology at Stanford University School of Medicine .

“We’re opening up a window onto a world of cognition that has been opaque to science until now,” added Newsome, who is also the Vincent V.C. Woo Director of the Wu Tsai Neurosciences Institute .

One decision at a time

Neuroscience studies of decision making have generally involved estimating the average activity of populations of brain cells across hundreds of trials. But this process overlooks the intricacies of a single decision and the fact that every instance of decision making is slightly different: The myriad factors influencing whether you choose to read this article today will differ from those that would affect you if you were to make the same decision tomorrow.

“Cognition is really complex and, when you average across a bunch of trials, you miss important details about how we come to our perceptions and how we make our choices,” said Jessica Verhein, MD/PhD student in neuroscience and co-lead author of the paper.

For these experiments, the monkeys were outfitted with a neural implant about the size of a pinky fingernail that reported the activity of 100 to 200 individual neurons every 10 milliseconds as they were shown digital dots parading on a screen. The researchers placed this implant in the dorsal premotor cortex and the primary motor cortex because, in previous research, they found that neural signals from these brain areas convey the animals’ decisions and their confidence in those decisions.

Each video of moving dots was unique and lasted less than two seconds, and the monkeys reported their decisions about whether the dots were moving right or left only when prompted – a correct answer given at the correct time earned a juice reward. The monkeys signaled their choice clearly, by pressing a right or left button on the display.

Inside the monkeys’ brains, however, the decision process was less obvious. Neurons communicate through rapid bursts of noisy electrical signals, which occur alongside a flurry of other activity in the brain. But Peixoto was able to predict the monkeys’ choices easily, in part because the activity measurements he saw were first fed through a signal processing and decoding pipeline based on years of work by the lab of Krishna Shenoy , the Hong Seh and Vivian W. M. Lim Professor in the School of Engineering and a professor, by courtesy, of neurobiology and of bioengineering, and a Howard Hughes Medical Institute Investigator.

Shenoy’s team had been using their real-time neural decoding technique for other purposes. “We are always trying to help people with paralysis by reading out their intentions. For example, they can think about how they want to move their arms and then that intention is run through the decoder to move a computer cursor on the screen to type out messages,” said Shenoy, who is co-author of the paper. “So, we’re constantly measuring neural activity, decoding it millisecond by millisecond, and then rapidly acting on this information accordingly.”

In this particular study, instead of predicting the immediate movement of the arm, the researchers wanted to predict the intention about an upcoming choice as reported by an arm movement – which required a new algorithm. Inspired by the work of Roozbeh Kiani, a former postdoctoral scholar in the Newsome lab, Peixoto and colleagues perfected an algorithm that takes in the noisy signals from groups of neurons in the dorsal premotor cortex and the primary motor cortex and reinterprets them as a “decision variable.” This variable describes the activity happening in the brain preceding a decision to move.

“With this algorithm, we can decode the ultimate decision of the of the monkey way before he moves his finger, let alone his arm,” said Peixoto.

Three experiments

The researchers speculated that more positive values of the decision variable indicated increased confidence by the monkey that the dots were moving right, whereas more negative values indicated confidence that the dots were shifting left. To test this hypothesis, they conducted two experiments: one where they would halt the test as soon as the decision variable hit a certain threshold and another where they stopped it when the variable seemed to indicate a sharp reversal of the monkey’s decision.

During the first experiments, the researchers stopped the tests at five randomly chosen levels and, at the highest positive or negative decision variable levels, the variable predicted the monkey’s final decision with about 98 percent accuracy. Predictions in the second experiment, in which the monkey had likely undergone a change of mind, were almost as accurate.

In advance of the third experiment, the researchers checked how many dots they could add during the test before the monkey became distracted by the change in the stimulus. Then, in the experiment, the researchers added dots below the noticeable threshold to see if it would sway the monkey’s decision subliminally. And, even though the new dots were very subtle, they did sometimes bias the monkey’s choices toward whatever direction they were moving. The influence of the new dots was stronger if they were added early in the trial and at any point where the monkey’s decision variable was low – which indicates a weak level of certainty.

“This last experiment, led by Jessie [Verhein], really allowed us to rule out some of the common models of decision making,” said Newsome. According to one such model, people and animals make decisions based on the cumulative sum of evidence during a trial. But if this were true, then the bias the researchers introduced with the new dots should have had the same effect no matter when it was introduced. Instead, the results seemed to support an alternative model, which states that if a subject has enough confidence in a decision building in their mind, or has spent too long deliberating, they are less inclined to consider new evidence.

New questions, new opportunities

Already, Shenoy’s lab is repeating these experiments with human participants with neural dysfunctions who use these same neural implants. Due to differences between human and nonhuman primate brains, the results could be surprising.

Potential applications of this system beyond the study of decision making include investigations of visual attention, working memory or emotion. The researchers believe that their key technological advance – monitoring and interpreting covert cognitive states through real-time neural recordings – should prove valuable for cognitive neuroscience in general, and they are excited to see how other researchers build on their work.

“The hope is that this research captures some undergraduate’s or new graduate student’s interest and they get involved in these questions and carry the ball forward for the next 40 years,” said Shenoy.

Stanford co-authors include former postdoctoral scholars Roozbeh Kiani (now at New York University), Jonathan C. Kao (now at the University of California, Los Angeles) and Chand Chandrasekaran (now at Boston University); Paul Nuyujukian, assistant professor of bioengineering and of neurosurgery; previous lab manager Sania Fong and researcher Julian Brown (now at UCSF); and Stephen I. Ryu, adjunct professor of electrical engineering (also head of neurosurgery at the Palo Alto Medical Foundation). Newsome, Nuyujukian and Shenoy are also members of Stanford Bio-X and the Wu Tsai Neurosciences Institute .

This research was funded by the Champalimaud Foundation, Portugal; Howard Hughes Medical Institute; National Institutes of Health via the Stanford Medical Scientist Training Program; Simons Foundation Collaboration on the Global Brain; Pew Scholarship in Biomedical Sciences; National Institutes of Health (including a Director’s Pioneer Award); McKnight Scholars Award; National Science Foundation; National Institute on Deafness and Other Communication Disorders; National Institute of Neurological Disorders and Stroke; Defense Advanced Research Projects Agency – Biological Technologies Office (NeuroFAST Award); and Office of Naval Research.

To read all stories about Stanford science, subscribe to the biweekly  Stanford Science Digest .

Separating movement from sight when studying the brain’s visual cortex

In primates, activity in the visual cortex—a part of the brain that processes signals from the eyes—is largely unaffected by the body’s own movements, according to a new study from scientists at the National Eye Institute (NEI). The findings allay recent concerns about the validity of prior research studying signals in the visual cortex, which failed to fully account for body movements.

“Our study also shows the importance of comparing research like this across species,” said Hendrikje Nienborg, Ph.D., chief of the NEI Visual Decision Making Section and lead author of a report about the study. 

Kang, Talluri and Nienborg

From left, Incheol Kang, Ph.D.; Bharath Talluri, Ph.D.; and Hendrikje Nienborg, Ph.D.

For decades, researchers have explored how our brains process visual information. During these experiments, the researchers often ignored the animals’ own body movements. However, animals interact with the environment by moving their bodies. In recent studies in rodents, researchers found neural activity from body movements throughout many areas of the brain, including the visual cortex. While it is possible – with modern technology – to account for these movement signals, many earlier studies in primates (including humans) have not directly done so. The findings from the rodent studies put the validity of years of research in doubt.

In a new study, scientists led by Nienborg, have found that, unlike in rodents, movement creates very little neural activity in the primate visual cortex. Further, the small amount of movement-related neural activity the scientists found turned out to be simply due to changes in visual information from the eye moving, not the movement itself.

Incheol Kang, Ph.D., and Bharath Talluri, Ph.D., in Nienborg’s lab, are co-first authors of the study. The research was supported by the NEI, the German Research Foundation, and the National Science Foundation.

Reference: Talluri BC*, Incheol K*, Lazere A, Quinn KR, Kaliss N, Yates JL, Butts DA, and Nienborg H. “Activity in primate visual cortex is minimally driven by spontaneous movements.” Nat Neurosci. 2023 Oct 12. doi: 10.1038/s41593-023-01459-5  

Nienborg, Kang and Talluri discuss the study's findings.

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  • v.24(4); 2015 Dec

Characteristics of Brains in Autism Spectrum Disorder: Structure, Function and Connectivity across the Lifespan

1 Department of Psychiatry, Institute of Behavioral Science in Medicine and Yonsei Autism Laboratory, Yonsei University College of Medicine, Seoul 03722, Korea.

In-Jung Sohn

2 Division of Child and Adolescent Psychiatry, Severance Children's Hospital, Yonsei University College of Medicine, Seoul 03722, Korea.

Namwook Kim

Hyeon jeong sim, keun-ah cheon.

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors (RRBs). Over the past decade, neuroimaging studies have provided considerable insights underlying neurobiological mechanisms of ASD. In this review, we introduce recent findings from brain imaging studies to characterize the brains of ASD across the human lifespan. Results of structural Magnetic Resonance Imaging (MRI) studies dealing with total brain volume, regional brain structure and cortical area are summarized. Using task-based functional MRI (fMRI), many studies have shown dysfunctional activation in critical areas of social communication and RRBs. We also describe several data to show abnormal connectivity in the ASD brains. Finally, we suggest the possible strategies to study ASD brains in the future.


Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication and restricted repetitive behaviors (RRBs). Recently, there have been some changes in diagnostic criteria of ASD in The Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 (American Psychiatric Association, 2013). Several diagnoses have been integrated into one dimensional diagnosis, or ASD. As well, three criteria of ASD; (1) qualitative impairment in social interaction (2) in communication and (3) restricted repetitive and stereotyped patterns of behavior, interests, and activities have been reconstructed two domains; (1) persistent deficits in social communication and social interaction (2) restricted, repetitive patterns of behavior, interests, or activities [ 1 ].

According to the Centers for Disease Control and Prevention (CDC) report, ASD affected nearly 1 in 68 children in the United States in 2014. In Korea, the prevalence of ASD was estimated to be 2.64% in school-age children [ 2 ]. The global prevalence of ASD has rapidly increased over time, however, the etiology for ASD has been poorly understood [ 3 ]. It is believed that ASD is a highly heritable disorder and that genetic susceptibility interacts with environmental factors in ASD etiology [ 4 ].

Neuroimaging is a powerful tool for in vivo study to investigate the brain structure and function. Since Horwitz et al. reported linkage of ASD to abnormal brain activity using Positron Emission Tomography (PET) [ 5 ], many brain imaging studies have been conducted and provided understanding the underlying neurobiological mechanisms of ASD [ 6 ]. As Lange et al. described ASD as a dynamic disorder with complex changes over time from childhood into adulthood [ 7 ], developmental perspective may help to understand some contradictory findings in ASD studies [ 8 ]. Therefore, it is meaningful to review about the ASD brain features depending on age. The objective of this review is to summarize recent findings from brain imaging researches and to show characteristics of ASD brains in terms of structure, function, and connectivity across the lifespan. By overviewing the previous researches, we will discuss abnormalities of ASD brains and will suggest the future directions of ASD research.


Since neuroimaging approach is one of the few methods that enable to make direct observation of the brain in vivo , Magnetic Resonance Image (MRI) studies have provided many implications of neurodevelopmental characteristics underlying ASD [ 9 ]. Although various results were shown from structural MRI (sMRI) studies over the past decade, there are abnormalities in gray and white matter with some regional brain differences between ASD and typically developing (TD) control [ 7 , 10 , 11 ]. Many sMRI studies have investigated volumetric and morphometric brain in order to examine atypical brain anatomy and neurodevelopment in ASD. Reviewing these findings provides insights into the neural substrates and autistic symptoms across the human lifespan.

Total Brain Volume

The most coherent finding is an accelerated total brain volume growth in early children with ASD around 2~4 years of age [ 10 ]. Many age-related studies have examined group differences in the total brain volume between ASD and TD. Fig. 1 is a plot of whole brain volume by age and group, ASD and TD control (TDC) [ 7 ]. As shown in Fig. 1 , findings generally have evidence of its atypical developmental trajectory with enlarged brain volume in younger individuals with ASD [ 12 ], but decreased volume or no difference in older individuals with ASD compared to TDC [ 13 ]. Although it has not been identified abnormal brain maturation during adolescence and adulthood in ASD, brain development during early childhood in ASD seems to be predominated by an enlarged brain volume of the frontal and temporal lobes [ 14 ] followed by arrested growth and a possible declined volumetric capacity of the brain after around 10~15 years of age [ 7 ].

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Regional Brain Structure

The pathological mechanism that represents an ongoing enlargement of the brain is unclear. Recent progress has evidence that early overgrowth of ASD brain is caused by an accelerated expansion of cortical surface area but not cortical thickness before the age of 2 years [ 15 ]. It was a meaningful finding because it showed potential for clarification of the neurobiological mechanisms that might be deficient in ASD. An early white matter differences in ASD brains might explain the brain being connected atypically [ 16 ]. Thus, accelerated expansion of cortical surface area of the gray matter in ASD seems to be associated with impaired maturation of the cortical white matter.

The constituent parts of the neural systems associated with clinical symptoms in ASD were examined by many studies. Specific core regions have been suggested to mediate clinical phenotypes of ASD such as the frontotemporal lobe, frontoparietal cortex, amygdala, hippocampus, basal ganglia, and anterior cingulate cortex (ACC) [ 17 ]. For example, abnormalities in (1) the inferior frontal gyrus (IFG, Broca's area), superior temporal sulcus (STS), and Wernicke's area might be related to defects in social language processing and social attention [ 18 ], (2) the frontal lobe, superior temporal cortex, parietal cortex, and amygdala might mediate impairments of social behaviors [ 19 , 20 ] and (3) the orbitofrontal cortex (OFC) and caudate nucleus have been associated with RRBs of ASD [ 21 ]. Although deficits in these regions seem to be general in ASD, some findings proposed that abnormalities in these brain regions are not peculiar to ASD and seem to be common in other disorders such as obsessive-compulsive disorder, general anxiety disorders, and schizophrenia [ 22 , 23 , 24 ].

Zielinski et al. measured cortical thickness in various regions of ASD brains and reported accelerated cortical thinning in individuals with ASD aged 3~39 years in a longitudinal study [ 25 ]. Findings from a vertex-based measurements study suggested that individuals with ASD tend to have thinner cortices and reduced surface area by age-related effects [ 26 ]. These findings point that a plot of cortical development is curvilinear across the human lifespan and there are evidences of abnormal cortical expansion during early childhood followed by rapid cortical thinning during adolescence and adulthood.

Cortical Area

Brain overgrowth in childhood of ASD mediates a significant difference in geometry of the brain. Several neuroimaging studies have examined other aspects of the cerebral cortex, such as cortical shape and sulcal patterns. Abnormalities in cortical folding might be caused by mechanical tension of axonal white matter fibers pulling force on the neocortex [ 27 ]. Since cortical gyrification seems to be associated with an expansion of the outer cortical layers relative to the microstructural deeper layers of the gray matter, atypical cortical folding in the brains of children with ASD have been observed in several studies [ 28 , 29 ]. These findings suggest that there is remarkably enlarged gyrification of the frontal lobe in children and adolescents with ASD [ 28 ]. Regional cortical folding is increased in bilateral posterior brain regions in individuals with ASD during early adolescence and adulthood [ 30 ]. Whereas, reduced local gyrification has been reported in the right inferior frontal and medial parieto-occipital cortices in children with ASD [ 31 ] and in the left supramarginal gyrus in individuals with ASD aged 8~40 years [ 32 ]. These various findings imply that the specific pattern of cortical gyrification has been altered across the lifespan and that genetic and environmental factors contribute to aspects of cortical geometry.


At a neuroimaging level, functional MRI (fMRI) and magnetoencephalography (MEG) enable the exploration of atypical brain functions of ASD. Many studies have shown that structural differences between ASD and TD are different depend on age. As structural differences are related to different functions of brain domain, it is necessary to observe the brain functions across the human lifespan. According to the DSM-5 diagnostic criteria, social communication impairments and restricted, repetitive patterns of behaviors, we will review recent studies about the atypical brain functions of ASD based on two core features in age-dependent manner.

Infants, Toddlers and Children

Social communication and social interaction.

Language development is a critical neurobiological process to communicate each other. Delayed language development is one of the early warning signs of ASD [ 33 ]. Children with ASD commonly show impaired language development that leads to social communication deficits. Some fMRI studies have examined the neurobiological differences of impaired language development between children with ASD and TD children [ 34 , 35 ]. Wang et al. used fMRI to examine the neurobiological deficits in understanding irony in high-functioning children with ASD. In contrast to previous studies showing hypo-activation of regions involved in understanding the mental states of others, children with ASD showed hyper-activation than TD children in the right IFG as well as in bilateral temporal regions. Greater activity children with ASD fell within the network recruited in the TD children and this may reflect more efforts needed to interpret the intention of a word. They concluded that children with ASD have impairments interpreting the communicative intention of others'. These results also indicated that children with ASD can recruit regions activated as part of the normative brain circuitry when task requires some degree of explicit attention to socially relevant cues [ 34 ].

Deficits in working memory are important aspects of ASD, as there are some studies suggesting relations between deficits in working memory and social communication impairments [ 36 ]. Using MEG, Urbain et al. revealed significant correlation between hypo-activation in the ACC and increased social communication impairments in children with ASD. They suggested that ACC has a critical role in the regulation of both cognitive and emotional processing [ 37 ].

The ability to perceive emotional facial expressions and to represent co-speech gestures are critical to social interactions and deficits of these abilities have been reported in previous fMRI studies in children with ASD [ 38 , 39 ]. ASD children are known to be less reinforced by positive social reward such as smiling. Some studies reported that impairments in social reward learning could result in social communication impairments in children with ASD [ 40 ]. As shown in Fig. 2 , Kim et al. found that children with ASD showed lower activation of the right amygdala, right STS, and right IFG than TD children when they stimulated with fearful face. For the happy face stimuli, children with ASD showed hypo-activation of the left insular cortex. They concluded that the deficits in social cognition of ASD children could be explained by the impairment of the capacity for visual analysis of emotional facial expressions, the subsequent inner imitation through mirror neuron system (MNS), and the ability of transmitting it to the limbic system and processing the transmitted emotion [ 39 ].

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Restricted and repetitive patterns of behavior, interests, or activities

Restricted, repetitive patterns of behaviors, interest, or activities indicate heterogeneous features of ASD. They include a wide range from stereotypies, echolalia, rituals, restricted interest, cognitive inflexibility, to excessive sensitivity to change [ 41 ]. Presence of RRBs is an important diagnostic criterion for ASD and it has been linked to differences in the striatum. Such RRBs in ASD are evident in infants with ASD [ 42 ], and persist in children with ASD [ 43 ]. Sharer et al. used fMRI to examine the functional differences about RRBs in children with ASD. In this study, children with ASD demonstrated hypo-activity in the brain regions such as STS and posterior cingulate cortex related to visuomotor sequence learning. They suggested that differences in the brain mechanisms may support initial sequence learning in ASD and can help explain behavioral observations of ASD associated impairments in skill development [ 44 ]. Response monitoring is an important process that involves abilities to evaluate, monitor, and adjust one's own behavior if it does not match an intended goal. Impairments in adjusting behavioral strategies may be critical in ASD because failure to adjust that may contribute to the RRBs [ 45 ].

Goldberg et al. examined the neural basis of error monitoring using fMRI in children with ASD. Compared to TD children, ASD children showed increased activities in the anterior medial prefrontal cortex (mPFC) and the left superior temporal gyrus (STG) during commission error (versus correct inhibition) trials. These results suggest a greater attention towards the internal emotional state associated with making an error in children with ASD [ 46 ].

Adolescents and Adults

Several models have been proposed to explain impairments in social communication and interaction of ASD. For example, deficits in theory of mind (ToM), facial expression processing, language processing, and many other models have been suggested [ 47 ]. Although several studies have suggested various brain regions and hypo- or hyper-activity associated with impairments in social communication and interaction of ASD, neural correlates have been thought to underlie the deficits with basis of evidence through diverse functional imaging studies for several decades [ 47 , 48 ]. The brain areas that have been associated with social communication and interaction are referred to as "social brain area". The social brain area includes the STS and its adjoining areas, such as the middle temporal gyrus, fusiform gyrus (FG), amygdala, mPFC, and IFG [ 39 , 48 ]. It is thought that the social brain areas play a pivotal role in social cognition and interaction, and abnormal activities in these areas are associated with clinical manifestation in ASD.

ToM, an ability to understand others' intention, predict others action and, if needed, imitate that, is important in social communication and interaction. Association of ToM and MNS has been demonstrated by previous studies [ 49 , 50 ]. Some differences between ASD and TD children in performing imitation task have been reported through several studies [ 51 ]. Moreover, some investigators have proposed impaired MNS has a critical role in ASD [ 52 ]. Williams et al. showed aberrant activity in adolescent with ASD during observing others' behavior and imitating in the right temporo-parietal junction which has been known to be associated with ToM [ 53 ].

Face recognition is a primary step and has an important role for social communication and interaction. Impaired facial processing is an early-emerging feature of ASD. Therefore several imaging studies have been interested in facial processing [ 48 ]. In the study performed to high-functioning adults with autism, Humphrey et al. showed hypo-activation in bilateral fusiform face area and occipital face area [ 54 ]. There have been other reports about the FG and occipital area. In the meta-analysis of social process in ASD, relative hypo-activities have been exhibited in the left FG and bilateral occipital lobe [ 48 , 55 ]. Dalton et al. also reported hypo-activation in FG in facial processing task in adolescent with ASD [ 56 ].

Language processing impairments in ASD have heterogeneous range from absence of communication to high-order communication such as pragmatic language deficits. Several neuroimaging studies have proposed that aberrant activations in the Broca's area (left IFG) and Wernicke's area (left STG) may play a critical role in impaired language processing in ASD [ 47 ]. In the functional imaging study performed by Kana et al., the left inferior and middle frontal gyrus, and left angular gyrus have been showed hypo-activation in adolescent with ASD compared with healthy control [ 57 ].

The neural correlates underlying RRBs have been investigated less than social communication and interaction even though RRBs contain diverse manifestation in ASD and has clinical significance [ 47 ]. RRBs symptoms are not usually manifested in the same way and they are changed variously over time. Some studies have reported that the RRBs are manifested differently depending on age. Watts et al. showed ASD children (the age of 18 months ~24 months) had more frequency and longer duration in RRBs than TD children [ 58 ]. However, the same results have not been showed in older subjects [ 59 ]. Younger ASD children showed more motor and sensory repetitive behaviors, while older ASD children had more complex behaviors [ 60 ]. Considering the nature and severity of RRBs that are not stable over time, putative neural circuitry underlying RRBs can be exhibited differently depending on developmental stage.

Deficits in executive cognitive function may be related to RRBs of ASD, especially impairments in control of inhibition and cognitive flexibility [ 47 ]. A few studies have suggested associations of RRBs with functional and structural alterations in cortical-basal ganglia circuitry [ 59 ]. Mosconi et al. reported neurocognitive disturbances in voluntary behavioral control and suggested alterations in the front striatal systems contribute to higher-order repetitive behaviors in adolescents with ASD compared to normal control group [ 61 ]. Thakkar et al. demonstrated response monitoring, which involves evaluating the outcome of action and adapting to the contexts, is important in RRBs of ASD. According to several studies, response monitoring has been considered to depend on the ACC. Thakkar et al. performed eye movement task consisted of pro-saccade and anti-saccade. ASD subjects significantly made more errors than HCs and showed functional abnormalities of the ACC that may contribute to RRBs. Compared with HCs, ASD subjects showed increased rostral ACC activation in both correct and error responses [ 45 ].

Abnormal sensory processing also has been proposed to relate to RRBs in ASD [ 62 ]. Clery et al. examined brain activity during performing the task consisted of continuous visual changing stimuli. Adults with ASD exhibited greater activity in the bilateral occipital cortex and in the ACC associated with smaller activation in the superior and middle frontal gyri than control groups. Atypical connectivity between frontal and occipital regions was also found in ASD brains [ 63 ].


The brain is a structural and functional system that has features of complex networks [ 64 ]. Early brain imaging studies have focused on region specific differences in activity however, accumulated data implicate an important role of brain network activity in the brain function [ 65 ]. Brain connectivity can be divided into functional and structural connectivity: temporal similarities of brain activity in multiple regions and physical connections between the brain regions. A number of studies using the brain imaging techniques such as fMRI and diffusion tensor image (DTI) identified abnormal brain connectivity in individuals with ASD. Long-range cortical hypo-connectivity theory has been largely supported by many investigators [ 66 , 67 ], even there are some opposite reports to show hyper-connectivity in ASD [ 68 , 69 ]. Other investigators also demonstrated that local-range hyper-connectivity [ 70 , 71 ], however, these results are controversial and the agreement on terminology, local- and long-range, is still lacking.

Thus, below we mainly focused on the global connectivity depending on the developmental stage.

Toddlers and Children

Since Biswal et al. detected low frequency fluctuations which means manifestation of functional connectivity (FC) of the brain in the absence of task [ 72 ], resting-state fMRI (rsfMRI) is widely used in neuroimaging field. Few studies have examined FC in children with ASD. Using rsfMRI, Di Martino et al. measured striatal FC in ASD children and revealed widespread excessive pattern of FC in striatal-cortical circuitry, relative to TD children. Increased FCs were shown in nearly all striatal regions, limbic cortex, insula and pons. It is likely that these ectopic circuits reflect developmental derangement rather than immaturity [ 73 ]. Uddin et al. observed hyper-connectivity within several large-scale brain networks such as salience, default mode, frontotemporal, motor, and visual networks in children with ASD compared with TD children. Using maps of each individual's salience network, they could discriminate ASD from TD with 78% accuracy. They also suggested that salience network may be a distinguishing feature in ASD children [ 74 ]. The default mode network (DMN) which includes the posterior cingulate gyrus, retrosplenial cortex, lateral parietal cortex, mPFC, superior frontal gyrus, and temporal lobe consistently has shown greater activity during resting-state than during cognitive tasks [ 75 ]. While the DMN has been identified as hypo-connected in adult ASD, the DMN-related circuits in ASD children were hyper-connected [ 76 ].

Structural connectivity (SC) can be measured using MRI-based DTI that provides information about white matter connection and the communication integrity. The study conducted with ASD toddlers using DTI demonstrated that frontal tracts displayed abnormal age-related changes with greater fractional anisotropy (FA) and volume than TD control. These deviant early development and age-related changes in frontal fiber tracts may underlie impaired social and communication behaviors in ASD [ 77 ]. Disturbances in the thalamo-frontal connections also reported in Korean boys with high-functioning ASD by our group. In this study, we firstly suggested involvement of the right anterior thalamic radiation (ATR) in ASD. As shown in Fig. 3 , FA was significantly decreased and mean diffusivity was significantly increased in the right ATR in subjects with ASD. These reduced FA was negatively correlated with total Social Responsiveness Scale (SRS), rating scale that quantifies the presence and severity of ASD symptoms [ 78 ].

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Using the fMRI, most studies suggested that hypo-connectivity in ASD during the task performance examining language [ 79 ], face processing [ 80 ] including emotional face [ 81 ], visuomotor coordination [ 82 ], working memory [ 83 ] and executive function [ 84 ]. Otherwise, there are some reports to show hyper-connectivity in ASD brains in language [ 68 ], visuomotor processing [ 85 ], selective attention [ 69 ]. Although the task-based fMRI studies have showed mixed results in both hypo- and hyper- connectivity, these results suggested that the functional connectivity in ASD brains differed from TD brains.

Resting-state studies have identified reduced connectivity in DMN that contains the brain regions relevant to social processing [ 36 ] and in right posterior STS that mediates deficits in emotional recognition in ASD [ 86 ]. Recently, Autism Brain Imaging Data Exchange (ABIDE), a consortium openly sharing existing rsfMRI data sets, was introduced. As shown in Fig. 4 , whole-brain analyses based on ABIDE reconciled disparate results of both hypo- and hyper-connectivity in the ASD literatures; both were detected, although hypo-connectivity dominated, particularly for cortico-cortical and interhemispheric functional connectivity [ 87 ].

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To measure SC, Peeva et al. collected DTI scan results from ASD adults and found that weaker connection between the left ventral premotor cortex, a region involved in speech motor planning, and the supplementary motor area in ASD group. These results indicated that an important pathway in the speech production network is impaired in ASD, and this impairment can occur even in individuals with normal language abilities [ 88 ]. Recent study examined alterations in the neuroanatomy of adults with ASD using two different modalities: sMRI and DTI. In ASD brains, gray matter (GM) volumes were decreased in multiple regions, including the bilateral fusiform gyri, bilateral orbitofrontal cortices, and bilateral pre- and post-central gyri. These changes in GM were linked with a decreased FA patterns in several white matter tracts, such as the bilateral inferior longitudinal fasciculi, bilateral inferior fronto-occipital fasciculi, and bilateral corticospinal tracts [ 89 ].

Recent findings from neuroimaging studies have led to the understanding of structural and functional abnormalities of the brain development in individuals with ASD, and the genetic bases of the brain development [ 90 ]. Synaptic deficits mediated by genetic factors in ASD not only affect their anatomical structure, but also affect the aspects of local neuronal circuitry and the functions of brain regions [ 91 ]. These are also related to the neuronal development and microstructural makeup of cortical folding. Differences in brain anatomy examined in ASD are relevant to specific clinical symptoms and features of ASD. ASD is likely a 'neural systems' condition that is mediated by abnormalities in regionally distributed cortical networks rather than separated brain regions. Therefore, ASD has also been referred to as a 'developmental disconnection syndrome' [ 92 ].

The clinical diversity of ASD phenotype might be also reflected on the level of brain structure and function. In this regard, it is important to know about the relationship among the structure, function and connectivity in the ASD brains. To elucidate associations between different aspects of the brain, multimodal imaging technique, the combination of multiple functional and structural measures can be a promising approach for investigating ASD brains.

Despite considerable evidence for abnormalities in ASD brain, there are inconsistent results from different groups. One of the reasons for these contradictory results is that researchers overlooked developmental changes in the brains [ 8 ]. In this regard, we introduce age-related changes of structure, function, and connectivity in ASD brains in this review. Brain is a complex organ that has been occurred dynamic changes over time as a normal developmental process [ 7 ]. Longitudinal studies will provide reliable information about atypical developmental patterns of the ASD brains. For this, national support on research and collaboration among the ASD family, researchers and clinician are necessary. The movement for open data sharing like ABIDE is a good example for worldwide collaboration.

Alternatively, the possible strategy is to define more homogenous subgroups of ASD. Individuals with ASD show enormous heterogeneity depending on age, gender, intellectual ability, genetic factor, and environmental risk factor [ 93 ]. Studies regarding these affective factors will bring more consistent data and improve understanding of neurobiological mechanisms of ASD.


This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI12C0021-A120029).

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Case Study: Child's Lobectomy Reveals Brain's Ability To Reorganize Its Visual System

After three years, patient recognizes faces normally, despite removal of preeminent regions involved in facial recognition.

By Shilo Rea shilo(through)cmu.edu

  • Dietrich College of Humanities and Social Sciences
  • shilo(through)cmu.edu
  • 412-268-6094

A new study led by Carnegie Mellon University neuroscientists provides the first evidence of how the human brain recovers the ability to function after losing parts of the visual system.

Published in Cell Reports , the researchers report on three years of behavioral and brain imaging tests on a nearly 7-year-old boy, "UD," who had a third of the right hemisphere of his brain removed in an attempt to control seizures. Even though the procedure left UD unable to see the left side, the team found that his brain's left hemisphere eventually compensated for visual tasks such as recognizing faces and objects.

"These findings provide a detailed characterization of the visual system's plasticity during children's brain development," said Marlene Behrmann , the Cowan University Professor of Cognitive Neuroscience in CMU's Dietrich College of Humanities and Social Sciences and the Center for the Neural Basis of Cognition . "They also shed light on the visual system of the cortex and can potentially help neurologists and neurosurgeons understand the kind of changes that are possible in the brain."

UD's entire occipital lobe, which includes the brain's visual processing center, and most of his temporal lobe, which receives both visual and auditory cues, were removed, leaving only two of the four lobes in his right hemisphere untouched.

To investigate how the lobectomy impacted UD, the researchers used fMRI testing at five different points over three years to evaluate how he performed certain visual and behavioral tasks. The researchers were surprised that the intact regions of UD's left hemisphere came to do the work of both hemispheres and process faces, objects and words.

Specifically, they found that UD's brain reorganized to compensate for some higher-order functions, like analyzing complex visual cues needed to recognize faces and words normally. However, it did not regain the ability to do lower-order functions, such as receiving and transmitting visual aspects from 180 degrees, which leaves the left side blind to him.

"The only deficit is that he can not see the entire visual field. When he is looking forward, visual information falling on the left side of the input is not processed, but he could still compensate for this by turning his head or moving his eyes," Behrmann said. "Moreover, by tracking the changes in the brain as UD developed, we were able to show which parts of the brain remained stable and which were reorganized over time. This offers insight into how the brain can remap visual function in the cortex."

Lobectomy procedures are rare, done on only 4 to 6 percent of patients of all ages with medically intractable epilepsy. UD, who is now almost 11, is free of seizures. As before the surgery, his IQ is above average and his language and visual perception skills are age appropriate.

While Behrmann hopes that this study can be used to inform more life-changing neural procedures, many critical scientific questions remain.

"More needs to be done to understand which lobectomy patients will show recovery, which will not and why not," she said. "It will also be important to know if patients are more likely to regain functions if the left or right hemisphere is removed and if the visual system is more robust in younger individuals."

The National Institutes of Health funded this research.

In addition to Behrmann, the research team included Carnegie Mellon's Tina T. Liu, Mark D. Vida, John A. Pyles, Ying Yang and Erez Freud, the University of Toronto's Adrian Nestor and Fan Nils Yang of Sun Yat-Sen University.

  • Washington Post: A 12-year-old had one-sixth of his brain removed. He feels ‘perfectly normal.’
  • Newsweek: Lobectomy Study: Scientists Reveal Boy's Incredible Recovery After Large Chunk of his Brain was Removed
  • PBS News Hour: A child lost a sixth of his brain, then made an amazing comeback
  • Cell Reports: Successful Reorganization of Category-Selective Visual Cortex following Occipito-temporal Lobectomy in Childhood

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Phineas Gage: His Accident and Impact on Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Author unknown / Wikimedia Commons

  • Phineas Gage's Accident
  • Effects of Injury
  • Severity of Brain Damage
  • Impact on Psychology
  • Post-Accident Life

Frequently Asked Questions

Phineas Gage is often referred to as the "man who began neuroscience." He experienced a traumatic brain injury when an iron rod was driven through his skull, destroying much of his frontal lobe .

Gage miraculously survived the accident. However, his personality and behavior were so changed as a result of the frontal lobe damage that many of his friends described him as an almost different person entirely. The impact that the accident had has helped us better understand what the frontal lobe does, especially in relation to personality .

Phineas Gage's Accident

On September 13, 1848, 25-year-old Gage was working as the foreman of a crew preparing a railroad bed near Cavendish, Vermont. He was using an iron tamping rod to pack explosive powder into a hole.

Unfortunately, the powder detonated, sending the 43-inch-long, 1.25-inch-diameter rod hurling upward. The rod penetrated Gage's left cheek, tore through his brain , and exited his skull before landing 80 feet away.

Gage not only survived the initial injury but was able to speak and walk to a nearby cart so he could be taken into town to be seen by a doctor. He was still conscious later that evening and able to recount the names of his co-workers. Gage even suggested that he didn't wish to see his friends since he would be back to work in "a day or two" anyway.

The Recovery Process

After developing an infection, Gage spent September 23 to October 3 in a semi-comatose state. On October 7, he took his first steps out of bed, and, by October 11, his intellectual functioning began to improve.

Descriptions of Gage's injury and mental changes were made by Dr. John Martyn Harlow. Much of what researchers know about the case is based on Harlow's observations.

Harlow noted that Gage knew how much time had passed since the accident and remembered clearly how the accident occurred, but had difficulty estimating the size and amounts of money. Within a month, Gage was well enough to leave the house.

In the months that followed, Gage returned to his parent's home in New Hampshire to recuperate. When Harlow saw Gage again the following year, the doctor noted that while Gage had lost vision in his eye and was left with obvious scars from the accident, he was in good physical health and appeared recovered.

Theories About Gage's Survival and Recovery

The type of injury sustained by Phineas Gage could have easily been fatal. While it cannot be said with certainty why Gage was able to survive the accident, let alone recover from the injury and still function, several theories exist. They include:

  • The rod's path . Some researchers suggest that the rod's path likely played a role in Gage's survival in that if it had penetrated other areas of the head—such as the pterygoid plexuses or cavernous sinus—Gage may have bled to death.
  • The brain's selective recruitment . In a 2022 study of another individual who also had an iron rod go through his skull—whom the researchers referred to as a "modern-day Phineas Gage"—it was found that the brain is able to selectively recruit non-injured areas to help perform functions previously assigned to the injured portion.
  • Work structure . Others theorize that Gage's work provided him structure, positively contributing to his recovery and aiding in his rehabilitation.

The Effects of Gage's Injury

Popular reports of Gage often depict him as a hardworking, pleasant man prior to the accident. Post-accident, these reports describe him as a changed man, suggesting that the injury had transformed him into a surly, aggressive heavy drinker who was unable to hold down a job.

Harlow presented the first account of the changes in Gage's behavior following the accident. Where Gage had been described as energetic, motivated, and shrewd prior to the accident, many of his acquaintances explained that after the injury he was "no longer Gage."

Since there is little direct evidence of the exact extent of Gage's injuries aside from Harlow's report, it is difficult to know exactly how severely his brain was damaged. Harlow's accounts suggest that the injury did lead to a loss of social inhibition, leading Gage to behave in ways that were seen as inappropriate.

Some evidence suggests that many of the supposed effects of the accident may have been exaggerated and that Gage was actually far more functional than previously reported.

Severity of Gage's Brain Damage

In a 1994 study, researchers utilized neuroimaging techniques to reconstruct Phineas Gage's skull and determine the exact placement of the injury. Their findings indicate that he suffered injuries to both the left and right prefrontal cortices, which would result in problems with emotional processing and rational decision-making .

Another study conducted in 2004 used three-dimensional, computer-aided reconstruction to analyze the extent of Gage's injury. It found that the effects were limited to the left frontal lobe.

In 2012, new research estimated that the iron rod destroyed approximately 11% of the white matter in Gage's frontal lobe and 4% of his cerebral cortex.

Phineas Gage's Impact on Psychology

Gage's case had a tremendous influence on early neurology. The specific changes observed in his behavior pointed to emerging theories about the localization of brain function, or the idea that certain functions are associated with specific areas of the brain.

In those years, neurology was in its infancy. Gage's extraordinary story served as one of the first sources of evidence that the frontal lobe was involved in personality.

Today, scientists better understand the role that the frontal cortex has to play in important higher-order functions such as reasoning , language, and social cognition .

What Happened to Phineas Gage?

After the accident, Gage was unable to continue his previous job. According to Harlow, Gage spent some time traveling through New England and Europe with his tamping iron to earn money, supposedly even appearing in the Barnum American Museum in New York.

He also worked briefly at a livery stable in New Hampshire and then spent seven years as a stagecoach driver in Chile. He eventually moved to San Francisco to live with his mother as his health deteriorated.

After a series of epileptic seizures, Gage died on May 21, 1860, almost 12 years after his accident. Seven years after his death, Gage's body was exhumed. His brother gave his skull and the tamping rod to Dr. Harlow, who subsequently donated them to the Harvard University School of Medicine. They are still exhibited in its museum today.

Phineas Gage Summary

In 1948, Phineas Gage had a workplace accident in which an iron tamping rod entered and exited his skull. He survived but it is said that his personality changed as a result, leading to a greater understanding of the brain regions involved in personality, namely the frontal lobe.

A Word From Verywell

Gage's accident and subsequent experiences serve as a historical example of how case studies can be used to look at unique situations that could not be replicated in a lab. What researchers learned from Phineas Gage's skull and brain injury played an important role in the early days of neurology and helped scientists gain a better understanding of the human brain and the impact that damage could have on both functioning and behavior.

Gage died from an epileptic seizure almost 12 years after the accident. These seizures started a few months before his passing, though his health had started to decline several months before the seizures began.

The damage occurred to Phineas Gage's frontal lobe, the region of the brain at the front of the head. The frontal lobe plays a role in our ability to speak, make decisions, and move. It is also partially responsible for our personality.

Post-accident, Gage's demeanor was said to have changed from pleasant to surly and he went from being a hardworking, motivated man to a man who had trouble keeping a steady job. Some reports suggest that Gage's personality changes were exaggerated, and that they may also have been temporary, fading a couple of years after the accident.

Phineas Gage lived almost 12 years after the rod pierced his skull. He died on May 21, 1860. This would make him just short of 37 years old at the time of his death.

Gage's accident helped teach us that different parts of the brain play a role in different functions. Through studying Gage's frontal lobe damage, we gained a better understanding of what the frontal cortex does with regard to personality. We also began to know more about the effects of frontal lobe damage and how it may change a person.

Sevmez F, Adanir S, Ince R. Legendary name of neuroscience: Phineas Gage (1823-1860) . Child's Nervous System . 2020. doi:10.1007/s00381-020-04595-6

Twomey S. Phineas Gage: Neuroscience's most famous patient .  Smithsonian Magazine.

Harlow JM. Recovery after severe injury to the head . Bull Massachus Med Soc . 1848. Reprinted in  Hist Psychiat. 1993;4(14):274-281. doi:10.1177/0957154X9300401407

Harlow JM. Passage of an iron rod through the head . 1848. J Neuropsychiatry Clin Neurosci . 1999;11(2):281-3. doi:10.1176/jnp.11.2.281

Itkin A, Sehgal T. Review of Phineas Gage's oral and maxillofacial injuries . J Oral Biol . 2017;4(1):3.

de Freitas P, Monteiro R, Bertani R, et al. E.L., a modern-day Phineas Gage: Revisiting frontal lobe injury . The Lancet Regional Health - Americas . 2022;14:100340. doi:10.1016/j.lana.2022.100340

Macmillan M, Lena ML. Rehabilitating Phineas Gage . Neuropsycholog Rehab . 2010;20(5):641-658. doi:10.1080/09602011003760527

O'Driscoll K, Leach JP. "No longer Gage": An iron bar through the head. Early observations of personality change after injury to the prefrontal cortex . BMJ . 1998;317(7174):1673-4. doi:10.1136/bmj.317.7174.1673a

Macmillan M. An Odd Kind of Fame: Stories of Phineas Gage . MIT Press.

Damasio H, Grabowski T, Frank R, Galaburda AM, Damasio AR. The return of Phineas Gage: Clues about the brain from the skull of a famous patient . Science . 1994;264(5162):1102-5. doi:10.1126/science.8178168

Ratiu P, Talos IF. Images in clinical medicine. The tale of Phineas Gage, digitally remastered . N Engl J Med . 2004;351(23):e21. doi:10.1056/NEJMicm031024

Van Horn JD, Irimia A, Torgerson CM, Chambers MC, Kikinis R, Toga AW. Mapping connectivity damage in the case of Phineas Gage . PLoS One . 2012;7(5):e37454. doi: 10.1371/journal.pone.0037454

Shelley B. Footprints of Phineas Gage: Historical beginnings on the origins of brain and behavior and the birth of cerebral localizationism . Archives Med Health Sci . 2016;4(2):280-6. doi:10.4103/2321-4848.196182

Garcia-Molina A. Phineas Gage and the enigma of the prefrontal cortex . Neurologia . 2012;27(6):370-5. doi:10.1016/j.nrleng.2010.03.002

Johns Hopkins Medicine. Brain anatomy and how the brain works .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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EPA Home » Science Inventory » An inter-laboratory case study to determine the added value of the Zebrafish Light-dark transition test to predict developmental neurotoxicity (DNT5)

An inter-laboratory case study to determine the added value of the Zebrafish Light-dark transition test to predict developmental neurotoxicity (DNT5)

Truong, L., J. Hsieh, L. Ellis, V. Schiavone, A. Alzualde, J. Legradi, D. Rubbini, C. Woodland, K. Ryan, B. Hill, S. Padilla, M. Behl, J. Terriente, A. Muriana, R. Tanguay, M. Sachana, A. Price, T. Shafer, AND E. Hessel. An inter-laboratory case study to determine the added value of the Zebrafish Light-dark transition test to predict developmental neurotoxicity (DNT5). 5th International Conference on Developmental Neurotoxicity Testing (DNT5), Konstanz, GERMANY, April 05 - 08, 2020.


The Organisation for Economic Co-operation and Develoment (OECD) building a guidance document containing a testing strategy to predict developmental neurotoxicity which, at present, consists of a combination of in vitro tests encompassing the critical processes in brain development. The aim of this study is to investigate the added value of the zebrafish developmental neurotoxicity testing behavioral model in this testing strategy.


Developmental neurotoxicity (DNT) entails one of the most complex areas in toxicology. Development of the central nervous system is a complex process involving many different events within strictly controlled time frames and therefore each event might create a different window of vulnerability to chemical exposure. OECD test guidelines for DNT (TG 426 and 443) are only occasionally carried out and the predictivity of these in vivo animal tests for human health effects may be limited. There is a high need for human-relevant in vitro models to assess DNT potential of chemicals. OECD is, therefore, building a guidance document containing a testing strategy to predict DNT. This testing strategy consists of a combination of in vitro tests encompassing the critical processes in brain development. The aim of this study is to investigate the added value of the zebrafish DNT behavioral model in this testing strategy. Up until 120 hours post-fertilization (hpf) zebrafish are not considered as experimental animals under the current European animal directive (2010/63/EU). The neurological system, the different neuron types, and neurotransmitters are well studied and well conserved among zebrafish and other species, including humans. The advantage of the zebrafish model in comparison to other in vitro assays is that whole brain development occurs within a relatively short period and effects of chemicals on brain development and behaviour can be tested. A group of experts agreed on a protocol for the light-dark transition test to predict DNT. At 120 hpf, zebrafish are tested in the light-dark transition test after chemical exposure from 6-120 hpf in a 96 well plate, 7 concentrations and 12 larvae per concentration. Twenty-eight known DNT compounds will be tested in five different laboratories. Data analysis will focus on locomotor activity (distance moved) of the larvae during the testing period. Benchmark dose analysis will be performed to determine the critical effect dose of each compound and for comparison of results across laboratories. Based on the results of this inter-laboratory case study, the robustness, biological domain and addition of the light dark-transition assay to predict DNT will be discussed and determined. Future experiments will test more compounds in the light-dark transition tests and the added value of other zebrafish DNT behavioral test tests will be discussed. The future goal is to add the zebrafish DNT assays to the OECD guidance document and to use this model to predict DNT. (This abstract does not necessarily reflect EPA policy).

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case study activity parts of the brain
  • The medulla oblongata : It helps control our automatic functions, like breathing, blood pressure, heart rate, digestion, etc.
  • The annular protuberance or pons This is the portion of the base of the encephalon that is located between the medulla oblongata and midbrain. It connects the spinal cord and the medulla oblongata to the superior structures in the hemispheres of the cerebral cortex and/or the cerebellum. It is used in controlling the brain's automatic functions and it has an important role in the awake-state levels and consciousness and sleep regulation.
  • The cerebellum : It is located below the brain and is the second largest structure in the encephalon. All of the information that is received by the body from its different sensory and motor pathways in the brain is integrated into the cerebellum, which is why its main function is controlling movement. It also helps to control posture and balance, as well as makes it possible for people to learn how to move, walk, ride a bike... Damages to this structure generally cause movement and coordination problems and issues controlling posture, as well as dysfunctions in some superior cognitive processes.

THE MIDBRAIN: It is the structure that joins the posterior and anterior brain, driving motor and sensory impulses. Its proper functioning is a pre-requisite for the conscious experience. Damages to this part of the brain are responsible for some movement problems, like tremors, stiffness, strange movements, etc.

  • Diencephalon: Located in the interior of the brain. It is made up of important structures like the thalamus and hypothalamus.
  • Thalamus: It is similar to the re-transmission station of the brain: it transmits the majority of perceived sensory information (auditory, visual, and tactile), and allows them to be processed in other parts of the brain. It is also used in motor control.
  • Hypothalamus: It is a gland located in the center area of the base of the brain that has a very important role in the regulation of emotions and many other corporal functions like appetite, thirst, and sleep.
  • Brain Cerebrum: It is known informally as the brain, which covers all of the brain cortex (fine layer of gray matter, wrinkled in grooves and folds), the hippocampus, and the basal ganglia.

Brain Structure

Brain anatomy and functions

In this area, we will look closer at the brain's anatomy and the functions of each structure

  • Caudate nucleus, which is a "C" shaped nucleus that is implied in voluntary movement control, although it is also implied in learning and memory processes.
  • Globus pallidus
  • Amygdala, which plays an important key role in emotions, especially in fear. The amygdala helps to store and classify memories and emotions.

THE HIPPOCAMPUS: A small subcortical seahorse shaped structure that plays a very important role in the formation of memory, both in classification and long-term memory.

THE CEREBRAL CORTEX: A thin layer of gray matter that grooves around itself, forming a type of protuberance, called convolutions, that give the characteristic wrinkled look to the brain. The convolutions are delimited by grooves or cerebral sulcus and those that are especially are deep are called fissures. The cortex is divided into two hemispheres, right and left, and they are separated by the interhemispheric fissure and joined by a structure called the corpus callosum which allows transmission between the two. Each hemisphere controls a side of the body, but this control is inversed: the left hemisphere controls the right side, and the right hemisphere controls the left side. This phenomenon is called brain lateralization.

  • Frontal lobe: The biggest lobe in the cortex. It is located in the front, right behind the forehead. It extends from the anterior to the central sulcus. It is the control center of you brain. The frontal lobe is involved in planning, reasoning, problem solving, judgement, and impulse control, as well as in the regulation of emotions, like empathy, generosity and behavior. It is linked to executive functions (Miller, 2000; Miller & Cohen, 2001).
  • Temporal lobe: It is separated from the frontal and parietal lobe by the lateral sulcus and the limits of the Occipital lobe. It is used in auditory and language processing, and is also used in memory functions and managing emotions.
  • Parietal lobe: It's located between the central sulcus and the parietal-occipital sulcus. This part of the brain helps to process pain and tactile sensation. It is also involved in cognition.
  • Occipital lobe: It is delimited by the posterior limits of the parietal and temporal lobes. It is involved in visual sensation and processing. It process and interprets everything that we see. The Occipital lobe analyzes aspects like shape, color, and movement to interpret and make conclusions about visual images.
  • Some authors talk about a fifth lobe, the limbic lobe: The limbic system is made up of various structures, among of which is the amygdala, the thalamus, the hypothalamus, the hippocampus, the corpus callosum, and a few others. The limbic system manages physiological responses to emotional stimuli. It is related to memory, attention, emotions, sexual instincts, personality, and behavior.

Squire, L.R. (1992) Memory and the hippocampus: a synthesis from findings with rats, monkeys and humans. Psychol Rev, 99, pp.195-231.

Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nat Rev Neurosci, 1 (1), 59-65.

Miller, E. K. y Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci, 24, 167-202.

Kosslyn, S.M. (1994) Image and brain: the resolution of the imagery debate. Cambridge, Mass; MIT Press.

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  1. Parts of the Brain Activity for Kids, Brain Diagram, and Worksheets for

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    all had lesions (damage) in a specific part of the left inferior frontal lobe, later called Broca's area. Damage to this area of the brain results in Broca's aphasia (uh-FAY-zha), in which a person has difficulty producing speech. People with Broca's aphasia can understand simple language, but their words are not properly formed,

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    Lesions allow the scientist to observe any loss of brain function that may occur. For instance, when an individual suffers a stroke, a blood clot deprives part of the brain of oxygen, killing the neurons in the area and rendering that area unable to process information. In some cases, the result of the stroke is a specific lack of ability.

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    Cerebrum The cerebrum (front of brain) comprises gray matter (the cerebral cortex) and white matter at its center. The largest part of the brain, the cerebrum initiates and coordinates movement and regulates temperature. Other areas of the cerebrum enable speech, judgment, thinking and reasoning, problem-solving, emotions and learning.

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    Practical application. Researchers use a variety of neuroimaging tools to study the brain. Computed tomography (CT) scans are oblique X-ray slices that show the density of brain structures. Magnetic resonance imaging (MRI) uses changes in electrically charged molecules in a magnetic field to form images of the brain.

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    As well, three criteria of ASD; (1) qualitative impairment in social interaction (2) in communication and (3) restricted repetitive and stereotyped patterns of behavior, interests, and activities have been reconstructed two domains; (1) persistent deficits in social communication and social interaction (2) restricted, repetitive patterns of beha...

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    Cerebrum Your cerebrum is the largest part of your brain and handles conscious thoughts and actions. Different areas within your cerebrum also have different responsibilities like language, behavior, sensory processing and more.

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    The human brain is one of the most complex organs in our body. It is made up of diverse parts or structures that carry-out different functions and work together using thousands of connections that connect the brain to the rest of the body. Below, we will give you a description of the brain's structure, its different parts, and how each part works.

  26. Parts of the Brain

    Parts of the Brain. Find out more about parts of the brain with this zombie-themed, science worksheet! This color-coded diagram and set of facts introduces readers to complex terms and trivia about the anatomy of the brain. This worksheet is a great nonfiction text example containing features of nonfiction texts, ideal for fifth-grade students ...

  27. Division of Nutrition, Physical Activity, Obesity (DNPAO)

    CDC's Division of Nutrition, Physical Activity, and Obesity (DNPAO) leads our nation's efforts to prevent chronic diseases by promoting good nutrition, regular physical activity, and a healthy weight. We work in places where people live, learn, work, and play. Short features highlighting topics related to nutrition, physical activity, and ...

  28. Parts of the Brain Activity for Kids, Brain Diagram, and Worksheets for

    Learn about the Parts of the Brain for Kids with this fun brain activity for kids and handy brain worksheets!Children will learn about the functions of the brain for kids as part of our human body lesson.Use this brain project with elementary age students in first grade, 2nd grade, 3rd grade, 4th grade, 5th grade, and 6th garde students. Simply print free brain worksheets pdf file with brain ...