The former is represented by the change in gray matter containing cell bodies that process information. The latter is represented by the change in white matter containing nerve fibers that transmit information. A representative inverted U-shaped developmental change is observed in the synaptic connection. An important finding regarding synaptic connections was made before neuroimaging technology became available. An inverted U-shaped developmental pattern that includes a phase of rapid increase in synapse density followed by a phase of synapse-elimination has been found in non-human brains, such as kitten and monkey Cragg, ; Lund et al.
In human research, a pioneering study was done by Huttenlocher who carefully examined the synapses in postmortem human brains and investigated synaptic density changes according to the stages of brain development. This study demonstrated the pattern of a series of changes: synaptic density begins to increase after birth, followed by a rapid increase during a certain time period until it peaks; then, it begins to decrease and reaches the density level of adults Huttenlocher, ; Huttenlocher et al.
In other words, synapses are excessively formed for a brief time, but the excessive, unnecessary synapses are pruned afterwards. Interestingly, the temporal change of synaptic density differs depending on the region of the brain Huttenlocher and Dabholkar, For example, in the visual cortex, synaptic density is reported to rapidly increase at the age of 2—3 months, peak at 4—12 months, and then decrease to the level of adults at the age of 2—4 years.
In contrast, in the prefrontal cortex, all of the above timings are delayed, and it is reported that the adult density level is achieved at the age of approximately 15—20 years. Recently, similar inverted U-shaped patterns have been reported when researchers measured the synaptic marker proteins Glantz et al.
All of this evidence suggests that the developmental curve of synapse density shows an inverted U-shape in humans. In favor of this view, neuroimaging studies have also demonstrated that the volume and the thickness of the gray matter show an inverted U-shaped curve in the development of human cerebral cortices Jernigan et al. During 1—2 years after birth, when synaptic density rapidly increases Huttenlocher, ; Huttenlocher et al.
In addition, the peak timing of an inverted U-shape in the development of gray matter volume varies across cortical regions Giedd et al. This collection of evidence suggests that the morphological change i. In contrast to the inverted U-shaped change discussed above, it is generally believed that myelin shows a linear developmental change Yakovlev and LeCours, ; Benes, Myelin is an insulator that is formed around the axon of a nerve fiber, and the presence of myelin makes efficient signal transmission possible.
In other words, the brain with advanced myelination is considered to be a mature brain that can process information efficiently and at a high speed. It is reported that there is little myelination in the brain of a neonate soon after birth. Therefore, efficient and high-speed information processing is hardly possible in the brain of a neonate. These results are in good agreement with the findings of the developmental change of myelination observed in the examination of postmortem brains before the development of the DTI technique Yakovlev and LeCours, ; Benes, As described above, the developmental curve of myelination gradually ascends with time.
Just as the volume of the gray matter fits an inverted U-shaped curve corresponding with synaptic density, the volume of the white matter which contains abundant myelin fits a linear curve corresponding with myelination.
The volume of the white matter increases nearly linearly with age, independent of brain region, and this increase is known to continue until the age of approximately 20 years old Giedd et al. In sum, the developmental change of the human brain seems to show at least two different dynamics: an inverted U-shaped curve and a linear curve. The former can be represented by the change in brain cell bodies that process information, and the latter may be represented by the change in nerve fibers that transmit information by connecting different areas of the brain.
However, as recently shown by Shaw et al. Thus, further neuroimaging studies are definitely needed to fully understand the structural development of the human brain. In the previous section, we gave a general view of the structural changes in terms of brain development. In this section, we first introduce evidence in the adult brain showing how the brain changes its structures in association with the development of particular cognitive functions, and we will then discuss the relationship between brain structures and cognitive functions from the developmental viewpoint.
In the adult brain, it has been shown that particular regions of the brain are enlarged or diminished depending on the degree of the trait or the ability of the individual Kanai and Rees, These are examples of what happens during the process of mastering certain skills over a long period of time.
The structural changes of the brain can also be observed in ordinary people. DeYoung et al. They found the following changes: persons with higher extroversion have a larger medial orbitofrontal cortex, which is important for reward processing, and persons with higher neuroticism have a larger amygdala and cingulate cortex, which are involved in the processing of negative emotion DeYoung et al.
The two types of developmental changes in brain structure both inverted U-shaped change and linear change seem to be deeply associated with development of cognitive functions.
Shaw et al. In this study, the thickness of the cerebral cortex of the brains of approximately healthy children and adults was calculated using structural MRI. At the same time, an IQ test, which measures general intelligence level, was conducted.
Participants were grouped into three categories based on their scores, i. In the superior group, the cortex was relatively thin at first, increasing rapidly to reach a peak at the age of approximately 11 years, and then rapidly thinning again.
Thus, this group showed rapid and large inverted U-shaped change in the gray matter thickness of the frontal cortex. Compared to the superior group, the change of the cortical thickness was relatively slow and small in the average group, and the thickness reached a peak earlier at the age of 7—8 years Shaw et al. Hence, how the gray matter thickness of the frontal cortex changes during childhood appears to affect intellectual level.
Some evidence shows that the marked development of a cognitive function occurs coincidently with the period of thinning in a certain cortical area Casey et al. For example, an increase in language vocabulary is associated with cortical thinning in the left dorsolateral frontal and lateral parietal regions at the age of 5—11 years Sowell et al. In addition, improvement of hand motor skill is associated with cortical thinning in the left primary motor cortex M1 during the same age period Lu et al.
Finally, improvement of cognitive control ability seems to be related to cortical thinning in the anterior cingulate cortex and the right inferior frontal gyrus at the age of 5—10 years Kharitonova et al.
Hence, we may assume that the formation and pruning of synaptic connections during childhood is the key to build efficient neural circuits in the cortex, leading to its excellent functioning, and marked development of a cognitive function may occur coincidently with the period of synaptic pruning.
A growing body of literature also shows the relationship between linear structural change in white matter and cognitive performance during development. For example, high anisotropy measured by DTI that reflects directionality or non-randomness of diffusion in the superior fronto-parietal cortices correlates well with memory capacity Nagy et al. In addition, higher anisotropy in the left temporal and parietal lobules correlates well with reading ability Nagy et al. These studies investigated the age-related developmental changes of white matter fibers, which are associated with the development of cognitive abilities.
However, one should always bear in mind that these age-related changes also include the effects of individual differences. The best way to purely evaluate age-related change is to conduct a longitudinal cohort study, in which one can follow the change across time in the same individuals e. Altogether, the research described above strongly indicates that developmental changes in the brain structure both inverted U-shaped change and linear change are deeply associated with development of cognitive functions.
In the previous section, we outlined two major structural developmental changes of the brain, inverted U-shaped change and linear change, both of which are associated with the development of cognitive functions in general. In this section, among various brain functions, we focus on studies regarding visual, facial recognition, and social cognitive functions, which are very important for humans, who lead social lives. Neonates have blurred vision immediately after birth, and their decimal visual acuity is considered to be about 40 times worse than that of visually normal adults Miranda, ; Mayer and Dobson, ; van Hof-van Duin and Mohn, Their visual acuity improves rapidly in the first 6 months after birth, and then improves more gradually, reaching the adult level at the ages of 4—6-years-old Mayer and Dobson, ; Courage and Adams, ; Ellemberg et al.
In addition to visual acuity, various visual functions, such as color perception and depth perception, develop greatly by the time of preschool.
The undeveloped visual functions in infants are likely associated with the immature structure and function of the eye, which receives visual information, and of the brain, which processes visual information and has not sufficiently developed.
Visual information sent from the eye is transferred to the cortical region, called the visual cortex, located at the rear end of the cerebrum. More than 50 years ago, Wiesel and Hubel demonstrated through animal experiments that the experience of seeing things is very important for the functions of the visual cortex to develop normally.
In animals, they showed that visual deprivation at a particular period after birth severely affects the development of normal vision. Here, we introduce a study that successfully captured, by imaging, a functional change occurring in the visual cortices of infants in whom plastic changes are ongoing Yamada et al. In this study, a flashing light stimulation was presented to infants aged 1 year or younger, and the response of the visual cortex to the photic stimulation was investigated with fMRI.
The results showed that fMRI BOLD signals increased in response to the photic stimulation in infants less than 8 weeks of age corrected for gestational age at birth. The pattern of these signals was the same as that seen in adults. In the adult brain, when neurons fire, local consumption of oxygen increases, and this oxygen consumption in the local blood flow leads to a transient increase of deoxygenated hemoglobin deoxy-Hb in the blood. However, as the blood flow continuously supplies, the amount of oxygen supplied eventually exceeds the amount of oxygen consumed.
This can wash a way the transiently increased deoxy-Hb so as to decrease its concentration, which enhances the positive BOLD signal. Exact neuronal mechanisms remain to be unveiled. However, one possible explanation is as follows.
The reversing pattern negative BOLD can be observed after approximately 2—3 months after birth when the synaptic density Huttenlocher et al. This suggests the possibility that the regional oxygen demand in infants is greater than that in adults Yamada et al. This may cause an imbalance between oxygen demand and supply, resulting in an increase of local deoxy-Hb, which is associated with the negative BOLD phenomenon.
However, since a different view also exists Kozberg et al. Although these studies Yamada et al. Watanabe et al. The result showed that the amount of oxygenated hemoglobin in the visual cortex significantly decreased during light stimulation.
This clearly demonstrated that the visual cortex of an infant shows opposite blood flow dynamics compared to adults even under awake conditions Watanabe et al. Interestingly, this type of negative BOLD phenomenon seen in the visual cortex in infancy and early childhood Born et al. Namely, this phenomenon seems to be a physiological change that is inevitable for the development of brain function.
This type of negative BOLD response in the visual cortex cannot be seen in the lateral geniculate nucleus LGN , which is a relay nucleus for the transmission of information from the retina to the visual cortex Morita et al. Morita et al. The results showed that while the MRI signal pattern in the visual cortex was reversed at the corrected age of 8 weeks, the MRI signal in the LGN in response to light stimulation was elevated independent of age Morita et al.
This may be related to the fact that the maturation of the LGN is almost completed at the time of birth see below. Although rapid developmental changes occur in the human cerebral cortical region after birth, it is reported that in the phylogenetically older cortical regions such as the LGN , rapid developmental changes occur during the fetal stage.
For example, the number of optic tracts that run from the retina to the LGN peaks at the fetal age of 16—17 weeks and then decreases Provis et al.
In addition, excessive formation of synapses in the LGN is reported to occur at the fetal age of 16—17 weeks Wadhwa et al. Thus, we may understand this as follows: at the time of birth, the LGN has already matured to the same level observed in adults; therefore, the level of MRI signal response to light stimulation in infants is the same as that in adults.
The above results strongly suggest that the negative BOLD response observed in the cerebral cortical region is caused by a rapid developmental change in the cerebral cortex, which is specific to infancy and early childhood.
In sum, during the growth stage, the metabolism of the cerebral cortex is greatly changing probably because of the change in synaptic connections. Thus, the change in metabolic activity may be an inevitable phenomenon for brain development. Faces are the most important and most common visual pattern for humans, who lead social lives. Although all faces have parts such as the eyes, nose and mouth similarly arranged, we can distinguish the faces of many people and recognize subtle changes in their expressions.
How then does the facial recognition function develop? Here, we give an overview of the neuroimaging studies that have investigated the development of facial recognition.
Thus, the component is considered specific to facial stimulation Rossion and Jacques, In children, de Haan et al. ERPs for upright faces of a human and a monkey were recorded in 3-, 6- and month-old children. An ERP component that responded more strongly to a human face than to a monkey face was observed in the occipitotemporal region in children of all ages.
However, this component showed a longer latency than the N of the adults and was observed approximately — ms after the presentation of the faces. This component can be considered the counterpart of the N in adults because the recorded brain region is the same, even though its latency is longer than that of the ERP in adults de Haan et al. This type of brain activity specifically evoked by visual stimulation of human face has also been reported in early infancy Tzourio-Mazoyer et al.
For example, de Heering and Rossion reported that the occipitotemporal face region of the right hemisphere in 4—6-month-old infants selectively responds to face stimuli but not to object stimuli. In addition, a PET study reported that even in 2-month-old infants, the occipital region of the right hemisphere, which is activated specifically by faces in adults, was more strongly activated when the infant was exposed to a face than when exposed to mere light stimulation Tzourio-Mazoyer et al.
These findings indicate that, within at least 6 months after birth, humans likely start to process human faces differently from other objects. However, the more elaborate facial recognition function does not completely mature within the first year of birth; it takes a much longer time to acquire adult-like processing. Indeed, the ERP component of primary school children is reported to still be different in some brain regions from that of adults.
The latency of the N component of primary school children becomes nearly the same as that of adults. However, the wave pattern of the component appears to be different until children become approximately 11 years old. Until that time, the waveform of the N component is not sharp and has two small peaks.
In contrast, when children become 12—13 years or older, the waveform becomes similar to that of adults, i. However, one must also bear in mind that such age-dependent N changes could be observed for non-face objects as well Kuefner et al.
Some fMRI studies also show that the neural system for facial recognition is not yet fully developed in the school-age period. Scherf et al. The results showed similar selective activity in response to objects and places among all groups, but selective responses to faces were significantly different between groups.
Namely, in the adult and adolescent groups, multiple brain regions, including the fusiform face area FFA , showed selective responses to faces, which were not observed in the group of children. This finding indicates that face-selective processing is immature in the brains of children Scherf et al. Differences in facial processing between elementary school children and adults are also reported in other fMRI studies Passarotti et al. Golarai et al. These neuroimaging studies on face recognition suggest that there is a significant difference between how school-age children process faces and how adults process them, even during passive observation without any cognitive load e.
This may provide a possible answer to the debate surrounding the behavioral development of face-specific visual processing.
Some researchers proposed that face-specific visual processing is fully mature at around 5 years of age, and the developmental improvement of face processing is due to the maturation of general cognitive abilities such as memory and attention Crookes and McKone, ; McKone et al. Conversely, other researchers suggested that the developmental improvement in face perception lasts into adolescence or even adulthood Mondloch et al.
The neuroimaging evidence described here seems to support the latter view of slow developmental changes in facial recognition functions. In this way, in approximately 1 year after birth, the human brain likely starts to process a human face differently from something that is not a human face. However, this is only the beginning of the development of face recognition. When we consider the above studies Passarotti et al.
Thus, the brain region that is specialized for face processing emerges along with development. The emergence of such brain region seems to align with the concept of cortical specialization Johnson, ; Cohen Kadosh and Johnson, One of the main challenges for future neuroimaging studies is the clarification of both structural and functional changes in the brain that are directly associated with the specialization process. In leading a social life, we frequently experience situations in which we speculate about the feelings of others.
The mental activity of speculating about the mental states intention, emotion, belief, etc. This mentalizing function is very important in leading a normal social life and interacting with many people; thus, many neuroimaging studies on mentalizing have been conducted in adults.
When and how does this mentalizing function develop? In the field of developmental psychology, the Sally-Anne task is often used as a test to examine the presence or absence of mentalizing ability Baron-Cohen et al.
In this task, children are presented with the following story by using a picture-card show or a puppet show. In this story, there are two characters: Sally and Anne.
Sally first hides a toy in a certain place in a basket , and Anne then moves the toy to a different place in a box while Sally is away. Thus, based on this behavioral evidence, the mentalizing ability is believed to be acquired at the age of approximately 4 or 5 years.
What is changing in the brain at this age? Liu et al. They recorded ERPs while 4—6-year-old preschool children performed a false belief task Liu et al. A slow negative component with a latency of approximately ms was detected in the left frontal lobe of the preschool children who demonstrated a high rate of correct answers to the false-belief task, also seen in adults; however, this component was not observed in the preschool children with a low rate of correct answers.
This research is highly valued as the first study to show a difference in brain activity, according to the presence or absence of mentalizing ability.
Unfortunately, however, it could not identify the changes in brain regions directly involved in mentalizing. Recently, Gweon et al. The researchers told stories to 5—year-old children and adults, depicting: 1 a situation that required understanding the psychological state of others; 2 a situation that required understanding social relations; and 3 a situation of simply physical relations, and simultaneously measured brain activity with fMRI.
The results showed that in children at any age, similar to adults, the mentalizing regions were activated when the participants were trying to understand the psychological states of other persons. However, in younger children from 5 to 8. The series of studies described here suggests that this specialization is immature even in preschool children who could pass the Sally-Anne task, a brain function specific to mentalizing begins to form in the school-age period, and selective brain activities related to this function are observed in the latter half of the school-age period and beyond.
Although the specialization of this function progresses in the school-age period and beyond, adult-like efficient information processing has not yet been established during this period.
For example, using cartoons, Wang et al. They found that the groups had no difficulty distinguishing irony from sincerity, and the activity in the medial frontal cortex increased in both the children and adults in the irony condition.
However, the activity in this brain region increased more in the children compared with the adults Wang et al. The observation that children require more brain activity than adults to perform a social cognitive task was also reported in a study of 12—year-old children Blakemore et al. In general, when the brain repeats a certain experience for a long time, it becomes unnecessary to fully recruit the activity in the particular brain regions that are used during the experience.
For example, one study showed that a top-level soccer player could move his foot by recruiting a smaller amount of brain activity BOLD signal in the foot section of the M1 Naito and Hirose, Thus, one may associate the recruitment of less motor activity to generate a well-practiced movement with efficient neural control of that movement. Here, we also raise the possibility that the smaller increase in the BOLD signal could be related to a reduction of synaptic activity due to enhanced synaptic efficacy in M1 through extended motor practice, as shown in a non-human primate study Picard et al.
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Ten lessons on the N Neuroimage 39, — Scherf, K. Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. In this issue, we begin with a basic review of imaging findings in neurotrauma, with Collin Herman and Chesney Oravec with my help putting together a thorough primer, bound to be helpful to emergency physicians and intensivists, as well as neurologists and neurosurgeons.
Although newer CT and MRI tools are becoming available, a foundation in emergent imaging promises to improve outcomes for neurotrauma. Acute-stroke care is an area where progress has been made by leaps and bounds. New thrombectomy and intervention trials have drastically changed the outlook for otherwise dismal conditions. Ultrasound as a tool has existed for decades, but only recently has it been accepted as an extension of our senses, so to speak, when it comes to clinical assessment.
Emergency and critical care physicians swear by it, and neuromuscular experts have made it a valuable weapon in their arsenal. Jared Hollinger and Vanessa Baute tell us about the applications of ultrasound in the early and accurate diagnosis of various nerve and muscle pathologies.
Our best technologies are still put to the test when it comes to the biggest malady known to human-kind, cancer. Newer MRI sequences are now widely available—knowing the scope of these is vital to decision making in diagnosis, treatment, and prognostication. Mona Shahriari comes together, once again with Parham Moftakhar to elucidate these sequences and make the reader aware and comfortable with what might otherwise remain radiology mumbo-jumbo.
Finally, we have an informative report from dementia expert Mary Koran about the usefulness of imaging for understanding dementia.
Although clinical and behavioral testing remain the cornerstones of dementia diagnosis, imaging and serum and cerebrospinal fluid markers are becoming important. Of particular interest is positron emission tomography PET using radiolabeled tracers that help characterize brain regions affected, thereby altering prognosis.
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