The role of Under/Over-plasticity in the
development of Autism Spectrum
Disorder.
Benjamin Germain
10396411
Supervisor: Cédric Koolschijn
Co-assessor: Hilde Guerts
MSc in Brain and Cognitive Sciences, University of Amsterdam
Cognitive Neuroscience track
Abstract
Diagnoses of Autism Spectrum Disorder (ASD) has dramatically increased over the past few decades. With increasing prevalence comes increasing scientific interest. Many different theories have been designed in an attempt to fully incorporate all of the ASD symptoms. One prevailing theory is the Connectivity theory, which is based on under-connected networks improper information transfer. This paper first discuss how abnormal under-connectivity is associated with ASD symptoms such as in the connectivity theory. Then, local over-connectivity will be included in order to gain a more compile view of ASD.
Ultimately discussing how abnormal neuroplasticity could result in under/over-connectivity; ultimately displaying phenotypical ASD symptoms. Neuroplasticity is a key deterministic factor in ASD due to its impact of structural and therefor effective connectivity. Incorporating the abnormal neuroplasticity process into new ASD models will help further to fully elucidate many of the neurobiological aspects behind autism.
Autism Spectrum Disorder
Over the past few decades, the diagnosed prevalence of Autism Spectrum Disorders (ASD) has drastically increased. During the 1980’s autism was believed to be a rare condition with about 5 in every 10,000 children being affected (Gillberg , Steffenburg , Schaumann, 1991). However, a current study from the Center for Disease Control states that the prevalence of Autism has risen to 1 in every 88 children (Baio, 2012). With general mental retardation being number one, ASD is now considered the second most common serious development disability in the U.S. There are three types of diagnoses that fall under ASD, which are Autism, Asperger’s syndrome and atypical autism. All three of these disorders are characterized by impairment in reciprocal social interaction, impaired verbal and-nonverbal communication, as well limited interest and repetitive behaviors. Differences between diagnoses can be seen during the developmental profile and severity of symptoms. (Raznahan, Bolton, 2008). Unfortunately, there is no collaborated theory of what causes autism, how to prevent it, or even how to treat it.
Symptoms
Abnormalities in autistic children generally appear during infancy, with most children not experiencing a distinct stage of normal development. All autistic children show signs and symptoms before the age of 3 years old. Autistic symptoms are characterized by a triad of deficits comprising impaired social interaction/communication, restricted repertoire of interests and repetitive behaviors. Sustained blatant impairment in social interaction, which may be accompanied by impairment in multiple non-verbal behaviors is seen (e.g. body posture, gestures and facial expressions). Autistic children may demonstrate a lack of spontaneous or fictional play, as well as cooperative playing. Development of speech can be delayed, typically resulting in abnormal rate, rhythm, pitch, intonation, and/or be accompanied by abnormal repetitive use of language. Children may also experience behavioral symptoms including abnormal responses to sensory stimuli, such as oversensitivity to sounds or touch, lack of fear response and attraction to certain repetitive tasks (World Health Organization, 1993). Additionally, children may exhibit more disruptive behavioral systems such as over activity, short attention span, aggression, impulsivity and self-mutilation. Wing & Wing (1971) describe repetitive behaviors including flicking, counting, tapping or continual repetition of information is also seen. They also describe more compulsive behaviors such as meticulous organization of items and a resistance to change. Additionally, children can show a range of other nonspecific problems such as temper tantrums, sleeping and eating disturbances, fear/phobias and aggression. Children with autism, especially with severe retardation, can also partake in self-injury. Many people with autism lack initiative, spontaneity and have difficulty organization leisure time. They also have problems applying concepts during decision-making in work, even work that is within their ability and do not modulate their behavior according to their social environment. While these deficits manifest in childhood,
they persist into adulthood, where a reduction in symptom severity is seen (World Health Organization, 1993).
Causes/Theories
Unfortunately there is no solidarity of theories pertaining to ASD. There are currently many theories being investigated, each taking a slightly different approach to what causes typical ASD symptoms. Many of these theories partially explain ASD, while none of them completely explain the phenomena. Here I will discuss some of the most prevalent and supported theories dealing with ASD.
Theory of Mind
The Theory of Mind (TOM) hypothesis in Autism focuses on social deficits in language and communication, along with some defined neurocognitive deficits. TOM approaches the altered social interaction seen in people with autism as an inability to interoperate another person’s mental state. TOM is described as the ability to infer another person’s mental state in content to others, with the act of inferring one’s own mental state known as ‘meta representation’ (Baron-Cohen et al.,1985). This theory emphasizes the ability/inability to infer mental sates, rather than the outwardly expressed emotions. Baron-Cohen et al., (1985) states that while a person with autism can directly observer another persons expressed emotions, they cannot infer how these expressed emotions relate to the other person’s mental state. This is achieved through a complex cognitive mechanism that TOM observers believe to be faulty in persons with autism. Typical humans use TOM in many social interactions to help explain and predict another person’s behavior. Therefore, according to Baron-Cohen, the observed deficits are due to autistic children and adults not properly implementing a TOM.
A difficulty with this theory is that there is no solid stipulation for what constitutes a ‘Theory of Mind’, which results in the inability to properly measure and determine TOM. The TOM hypothesis has also been criticized on the point that infants show signs of autism before a TOM would develop normally. DeGelder (1987) argues that due to the development of signs during infancy, autism is more likely of biological origin.
Central Coherence Theory
The Central Coherence Theory (CCT) is characterized by a specific imbalance of information processing across levels (Frith & Happe, 1994). Normative processing involves central coherence by in cooperating many complicated pieces of sensory information into a single higher-level context. The CCT states that autistic children have disrupted central coherence ability. Meaning that a person with autism acquires all the normal sensory information, however, an unknown malfunction impedes the brains ability to successfully incorporate all of the sensory information into a single higher-level context. This theory has
been supported when looking into ‘idiot savants’. An autistic ‘idiot savant’ usually shows typical autistic symptoms; however, they have advanced abilities in specific tasks when compared with a normative control as well as other autistic subjects. Savantism is most common in people with autism; however, it can be expressed in non-autistic people as well (Treffert, 1988). CCT implies that these savants are advanced in specific fields due to their variation in information processing. Demonstrating that a savant may be able to focus
extensively on one sensory aspect, yet fail to incorporate multiple aspects together. The CCT also gained initial support by explaining some of the executive functioning deficits seen in people with autism, such as, when they show no reaction to a specific stimulus, unless the stimulus is in proper context. Lack of contextual recognition helps to demonstrate a
communication error between brain regions. According to Frith and Frith (1999) recognition of changes in emotional expression, status, relation and other individuals develop in monkeys before achieving meta representation. These abilities are believed to depend on a complex object recognition system supported by the ventral system. The ventral system is known to function as part of the reward system, drug addiction, motivation, cognition and is the focus of several psychiatric disorders (Schultz et al., 2000). A functional error within the ventral system (or other subcortical regions) helps to explain the cognitive deficits seen during infancy.
However, CCT fails to explain the lack of meta-representation as discussed in TOM. CCT discusses an abnormal collaboration of sensory information into a singular outer world view. However, this fails to account for the lack of meta-representation, as discussed in TOM, due to attributing the phenotypical symptoms to improper sensory input. If improper sensory input was the only malfunction, then a person with autism should not have a problem with meta-representation as it is disjointed from sensory input.
Connectivity Theory 2 21031 53381310 and 8912
Belmonte et al., (2004) discusses the integration of previous theories into an inclusive connectivity based theory. Connectivity denotes many different aspects of neurological activity and has been divided into two distant subtypes. The first type is known as functional connectivity, which is the statistical dependencies of activation among remote
neurophysiological events (Friston, 2011). Meaning, how likely is it for a particular area to be activated via remote neurological activity. Functional connectivity is observable and
quantifiable by measures such as coherence, correlation or transfer entropy (Friston, 2011). The second type of connectivity is known as effective connectivity, referring to the synaptic or network influence that one neural system exerts onto another. Meaning that, effective
connectivity indicates the efficacy of information transportation in networks. Effective connectivity is dynamic, based on a model of interaction and is used in order to explain observed dependencies (functional connectivity) by focusing on the influence between areas. In addition to these two distinctions, structural connectivity is also investigated. Structural connectivity is the anatomical connections within and between networks, as demonstrated by
increased synapses and tracts. Structural connectivity is not considered a complete
description of connectivity; however, it regulates effective connectivity, making it an essential factor to understand.
Functional differences in an autistic brain consist of high local connectivity in tandem with low long-range connectivity (Just et al., 2004). This could be a key reason in why a person with autism cannot differentiate signal from noise easily. Local brain areas have high effective connectivity and therefore can adequately process the stimuli with in the proper brain regions, however, low long-range effective connectivity hinders the information processed by the local areas from being fully integrated into a centrally coherent higher-level context.
fMRI studies observing the local hyperconnectivity and long range hypoconnectivity during an active visual search task, has shown abnormal over-activation to stimuli within its coinciding region(Shakula et al., 2010). Additionally, abnormal under-activation was seen on stimuli tasks designed to incorporate multiple different regions of activation (Belmonte and Yurgelun-Todd, 2003).
Currently the connectivity theory is in deliberation of how accurate the model actually is. While a substantial amount of studies conclude long range under-connectivity. There are disputing studies about local connectivity; with some studies claiming over-connectivity and other studies claiming under-connectivity (fully discussed later). Additionally, the next recent theory to be discussed states that symptoms of autism are due to local over-connectivity only.
Intense world theory
One of the most recent attempts at a unifying theory of autism is known as The Intense World Theory. This neurobiological theory that was first discussed by Markram, Rinaldi & Markram (2007), incorporates an abnormal structural/effective connectivity. Stating that “a common molecular syndrome causes excessive neuronal information processing and storage in the microcircuits of the brain” (p.87). According to this model, the cognitive phenotype seen in cases of autism can be accounted for due to abnormally increased effective connectivity resulting in hyper-perception, hyper-attention and hyper-memory. The theory states that due to increased structural connectivity, some of the brain’s microcircuits are over-developed and are subsequently over-activated. Such as in the neocortex, where increased effective connectivity is seen to produce excessive excitation due to regionally confined increase in plasticity (Markram et al., 2007). Once these microcircuits are defined, they can become autonomous, difficult to control and coordinate, especially between regions
(Markram et al., 2007).
The increased effective connectivity component may also contribute to the
exaggeration of tasks by giving further hyper-attention to the task. A positive consequence of this would be the exceptional capability to do a specific task, such as with ‘idiot savants’. The increased effective connectivity can reinforce this one task over and over until it becomes the
strong typical savant trait we know. However, the negative aspect of this component is that it can reduce the amount of behavioral routines to a minute amount, that is then repeated excessively. The continual increase in effective connectivity reinforces the microcircuits dealing with that specific attentional task. As these tasks gain stronger and stronger connections (due to ongoing plasticity) they will become dominate tasks, to the point where other tasks cannot be comprehended.
Lack of social interaction in this model is explained not by an inability to properly process social and emotional cues, but because some cues are overly intense, compulsively attended to and excessively processed. Resulting in the idea that: autistic people are neither lacking a TOM nor empathy for other, but are hyper-aware of everything, making social interactions to intense and therefor avoided. Resulting in a person with autism being
constantly bombarded with over attended stimuli information, making it difficult to function in a normal environment, hence the name Intense World theory.
While this theory incorporates increased local effective connectivity and uses such to explain the typical ASD symptoms, this theory does not account for decreased long-range effective connectivity, which has been seen in many ASD studies. It also does not discuss many of the cognitive abnormalities see in people with autism. Like with the CCT, meta-representation should still be present in a person with over-active sensory information. Due to the emphasis on sensory information overload, the intense world theory does not fully
elucidate observed cognitive aberration.
Anatomical brain variations
A key investigative interest in support of connectivity based approaches is the anatomical variation seen between autistic and normative brains. A post-mortem study on a 4 year old autistic male demonstrated a ~20% increase in overall brain weight compared with a typically developed child (Bailey et al., 1998). The analysis also exhibited disproportionately low brainstem and cerebellum, abnormal convolution cortex patterns, enlarged
over-convoluted temporal lobes and upwardly rotated hippocampi. The medulla oblongata was large, however, the pyramidal cells were relatively small. There were also differences seen with in the inferior olives, superior cerebellar vermis, pontine tegmentum, midbrain, raphe nuclei, and periaqueductal grey matter (Bailey et al., 1998). As post-mortem studies on children with autism is rare, magnetic resonance imaging (MRI) has been used to study anatomical brain structure.
MRI morphometry reveals an excessive volume of cerebrum and cerebral white and grey matter (Herbert et al., 2003, Sparks et al., 2002) and overall increase in brain volume (Aylward et al., 2002) in many (but not all) autistic children.This increased brain volume is not seen in adults (Aylward et al., 2002), indicating that the child goes through a stage of rapid growth followed by a plateau, where normative subjects catch up. Head circumference measurement analysis suggests that this time of overgrowth appears between 6-14 months
postnatal (Courchesne et al., 2003). This time in a typically developing child is a period of exuberant synaptogenesis, ongoing myelination and dendritic arborization. The largest increases are seen in the frontal lobes, with the dorsolateral convexity showing significant overgrowth; and the smallest increases are in the occipital lobes, with the precentral gyrus and orbital cortex not being heavily effected (Carper and Courchesne, 2004).
A comprehensive investigation (586 longitudinal and cross-sectional MRI scans) of the anatomical variations in autistic brains between ages 12 and 50 by Courchesne et al., (2011), has elucidate three phases to the abnormal brain growth, when compared with a normal subject. The first stage is mostly seen between ages 2-4 and is denoted by an
increase in overall brain volume in people with autism when compared to a normative subject. The second stage is a plateau stage, where the autistic brain retards growth, and a typically developing child’s brain begins to rapidly grow. The third stage is expressed by a premature accelerated rate of decline in the volume of an autistic brain, resulting in the autistic brain becoming smaller than a normative brain by approximately age 8 (see figure 1). Since anatomical variations are accompanied by a time of abnormally accelerated growth, followed up by a premature arrest of growth; Courchesne et al., (2011) states that the variance in-between autistic and normative brains is due to abnormal structural over-connectivity. Additionally, the same lab group conducted a stereological pilot study on children with autism which found more neurons (up to 43% more in a 2 year old autistic male compared with a 3 year old typical developing male) in the dorsolateral prefrontal cortex of every autistic male the group has analyzed so far. The magnitude of this excessive amount of neurons can only be accounted for prenatally, since no known postnatal neurobiological mechanism in humans can account for this rapid, seemingly global increase in the amount of neurons. Neurogenesis (the production of undifferentiated neurons) occurs in very few discrete areas in the adult brain such as the walls of our ventral plexus, is considerably slower and producing less cells (Ehninger et al., 2008).
Fig. 1 – Brain volume chart through 16 years old. Demonstrating an initial increase during 2-4
year in autistics and a decrease in brain volume for autistics 8-16 compared to a normal brain (from Courchesne et al., 2001).
One of the most common sites of anatomical abnormalities in autism is seen in the cerebellum. MRI morphometry revels hypoplasia of the cerebellar vermis and a cerebral volume increase of 9.76% in autistic children, when compared with normal developing children (Sparks et al., 2002).The study showed that the participants in the study had on average a increased hippocampus by 8.54% for the right side, and 9.16% for the left. The amygdala, which is vital for emotional processing and memory, shows an average increase of 16.67% on the right and 13.64% on the left all for autistic children in the study (Sparks et al., 2002). Additional studies have shown an association between degree of overgrowth in the amygdala and the severity of clinical symptoms (eg. Munson et al., 2006, Mosconi et al., 2009, Schumann et al., 2009).
A dual analytic MRI voxel-based morphometry (VBM) and cortical thickness analysis study demonstrated young male (~23 years old) autistic brains showing significantly thicker cortex in the frontal area, Brodmann area, temporal area, superior temporal sulcus, parietal, occipital, cingulate gyrus and fusiform gyri. While the autistic group showed three areas of significantly thinner cortex; precentral gyri, postcentral gyri and paracentral gyrus (Hyde et al., 2010). VBM analysis showed significant increases in grey matter concentration in the frontal brain areas as well as the brainstem for autistic brains. While showing a decrease in grey matter in the precentral and postcentral gyri. People with autism also showed a decrease in white matter within the anterior cerebellum and brainstem (Hyde et al., 2010).Figure 2 shows
a meta-analysis comprising grey matter overgrowth in people with autism. Growth and development of the neuronal structures in the brain is known as neuronal plasticity. As a result, it has been hypothesized that the anatomical variations observed here are due to a malfunctioning plasticity system(Belmonte et al., 2004, Boulager & Shatz, 2004).
Fig. 2 – Grey matter overgrowth in autism. The bars represent abnormalities in different
cerebral regions (standard deviations from normal average) in children and adolescents with ASD. Asterisks indicate statistically significant differences (p < 0.05) in autism as compared to normal control. References: (1) Carper et al., 2002, 3.4 years; (2) Bloss and Courchesne, 2007, 3.8 years; (3) Kates et al., 2004, 7.6 years; (4) Palmen et al., 2005, 11.1 years; (5) Hazlett et al., 2006, 19.1 years; (6) Schumann et al., 2010, 2–4 years (from Courchesne et al, 2007).
Neuroplasticity
Neuroplasticity is a general term that encompasses both synaptic and non-synaptic augmentation of the central nervous system. It refers to changes in neural pathways, synapses and glial cells. Neuroplasticity is known to occur due to changes in environment, development, behavior and bodily injuries. These changes can occur from on a small synaptic scale to larger cortical mapping. Previously, it was believed that neuroplasticity only occurred in children during pre/postnatal development. However, it has been shown that plasticity is seen though out adult life in the form of structural changes in axons, spines, and dendrites. Albeit, adult plasticity is known to take effect slower, as well as lack important features evident during developmental plasticity (Ehninger et al., 2008). The role of
neuroplasticity is widely recognized in healthy development, memory and learning. Disruption of the neuroplasticity process has been shown to results in aberrant development (Helt et al., 2008, Johnson et al., 2002, Caroni, Donato, & Muller, 2012). ‘When a developing brain is confronted with an abnormal constraint on information processing, it will evolve an abnormal organization in order to accommodate that constraint, resulting in a succession of autistic behavioural abnormalities extending into sensory, motor and later developing cognitive functions.(p.648)’ (Belmonte et al., 2004). If a child begins creating alternate experiences and processing strategies, plasticity can become maladaptive. This could cause the child to become an expert in the maladaptive strategies and ameliorate the typical strategies (Helt et
al., 2008). The mastered mal adaptive strategy will then become a permanent information processing strategy, if is not correct before the child’s plasticity reduces.
The study of neuroplasticity is relatively new and subsequently much of the details behind the plasticity process remains elusive. An article by Kolb and Gibb (2013), attempts to identify general principals of plasticity, in order to help consolidate our scattered
understanding of the process. The first item identified is that plasticity is found in all mammals/birds, and that the essence regarding the process is mostly universal between humans and other animals (e.g., Ardiel & Rankin, 2010). This conservation of the plasticity principals allows investigators to use a wide range of animal models in order to elucidate the human process.
Kolb and Gibb (2013) next discuss that the primary function of neuroplasticity is to change neuronal network organization. Stating that behavioral changes are related to modifications in the synapses within ensembles of connections (e.g., Caroni, Donato, & Muller, 2012), and that most behavioral changes are related to both the addition and the subtraction of synapses within a neuronal network.
The final component that Kolb and Gibb (2013) discuss is the three different types of plasticity. The first type of plasticity is known as experience-expectant. This occurs most commonly during early childhood development and is when different brain systems require specific types of co-functioning internal and external experiences. During particular times internal genetic coding increases plasticity. During this specific time, coinciding external experiences must occur in order to achieve typical development. This type of plasticity adapts the neuronal network in accordance with specific external stimulation/situations. A fine example of this is when a child learns language.
The second form of plasticity is known as experience-independent. This type of plasticity is also commonly seen during childhood development, however, specific experiences controlled by genetic coding is not necessary (unlike during
experience-expectant). Here we see an excessive growth of undifferentiated synapses and axons. During
this stage, children with typical exposure, experience many new life events, consisting of both internal and external stimuli. The neuronal networks and connections that are most typically activated will strengthen their connection in the form of additional and/or stronger synapses. The additional growths that are not frequently activated may begin to experience synaptic pruning, thereby weakening or eliminating the inactivated connection. An aphorism typically used with that applies strongly with experience-independent plasticity is “neurons that fire together, wire together”. Simply meaning, a neuronal cluster that frequently activates
contemporaneously, is more likely to build stronger physical connections (synapses, dendrites and axons) between neurons within that cluster. This type of plasticity is dependent on an external experience to differentiate necessary from unnecessary neuronal networks.
Finally the third type of plasticity is known as experience-dependent. Stating that experiences can cause changes in the brain, which are needed to modify established
neuronal ensembles. Such as in the case of phantom limb phenomenon. A phantom limb can occur after a person has had a limb amputated. At times, the person can still “feel” and try to use the missing limb. In some people, phantom limbs can be excruciatingly painful, with few functional remedies. Once believed to be a mental disorder, recent studies have
demonstrated that it is the result of plastic changes in both the periphery as well as the central nervous system (Flor, Nikolajsen, & Jensen, 2006). After amputation, increased structural and effective connectivity of neurons is seen in the dorsal horn of the spinal cord, as well as changes in the thalamus, brainstem and cortex are seen.
Plasticity- Animal Models
Due to overlap between animals and humans, extensive testing has been conducted on animal models in an attempt to elucidate the human plasticity process. Such as in the classic study by Wiesel and Hubel (1963), in which they blinded one eye of a kitten at birth. They then noticed the functioning eye expanding its neuronal system in order to compensate for the blinded eye. When the blinder is removed from the second eye, plasticity is again seen in the form of compromised growth between the two eyes, returning to equilibrium. This however, only would occur if the blinder was placed on the kitten within a specific time window after birth (Wiesel and Hubel, 1963). Also in the case of an injury, such as a mouse loses a whisker, adaptive remolding is seen in the associated receptive fields (Feldman & Brecht, 2005). The brain will begin to remodel (experience-dependent) the previous structure in order to accommodate for the new altered experiences (altered whisker sense).
Mouse modeling has also elucidate the varying effects of plasticity dependent upon age. Figure 3 below demonstrates the differences in the effects of plasticity, dependent on at what age a mouse received a medial prefrontal injury. The studies show that the amount of functional and behavioral recovery relates to at what time point the injury was received postnatal. The earliest injuries between P1-6 caused severe -debilitation and dismal
functional outcome. The mice that received injuries between P7-35 days postnatal resulted in dendritic and spine growth and functional recovery, on the level of normative responses in some cases. The mice that received the injury after day 55 showed little to no structural plasticity increase, resulting in a poor functional outcome. The mice that received the injury post day 120 showed signs of plasticity, as well as a partial return in function, yet less functional recovery than seen in the mice injured between P7-35. The positive behavioral outcomes from the later injuries are associated with plastic changes such as increased spine density and dendritic length (Kolb and Gibb, 2013).
Fig. 3: Summary of the effects of medial prefrontal injury at different ages, P= Post natal days (Kolb and Gibb, 2013).
Sensitive and Critical Periods
Sensitive periods are segments of time in which certain capacities are readily shaped and altered by experience. Critical periods are a special type of sensitive period, in which a particular trait must be learned. If the trait is not learned within the time period, it will never be mastered (Hubel and Wiesel, 1970). Traits learned in the critical period are thought to be permanent throughout life. An incorrect establishment made during a critical period cannot be ameliorated through exposure to the proper experience. These traits are essential as they become the foundation for future learning.
Humans have demonstrated these critical periods, such as in learning language in social contexts (learning a first language naturally). A prime example of a time that
demonstrates critical period like plasticity in humans is phonetic learning. Absence of exposure during early development (before 6 years old) to speech, natural language or sign results in an absence of normal language (Mayberry & Lock, 2003). Also, early experience with a particular language has enduring effects on speech perception. A study conducted on deaf children born to hearing parents showed that the children who began learning sign language after 6 years of age show a lifelong impairment to learning language (Mayberry & Lock,2003). The study demonstrated that language experience during early life assists in the development of the ability to learn a second language, independent of sensory-motor modality (Mayberry & Lock, 2003). Additionally, the lack of language experience during early life development severely compromises the development ability to learn a second language. A BOLD fMRI tactile discrimination study was conducted on the primary visual cortex (V1) in congenitally blind participants of varying age (Sadato et al., 2002). V1 activation was present
in blind subjects who lost their sight before 16 years of age. However, V1 was suppressed in blind subjects who lost their sight after 16 years of age. Sadato et al., (2002) states “that there is a critical period from birth to approximately 16 years of age for reorganization of the V1 to function during tactile discrimination tasks. (P.395)”. For ethical reasons (the possibility of causing permanent learning disabilities), much of the research conducted on critical periods is done in animal models. Luckily, according to Kolb and Gibb (2013), much of the plasticity process is universal between humans and other animals; resulting in the accessible translation from animal models to humans.
Animal Critical periods
Filial imprinting is a common example of a critical period. This is when a new infant must learn to recognize their mother/father figure. Since the newborn does not know the identity of it’s parents, the infant establishes a connection with the person who is consistently nearby and that satisfies its best innate expectations for the characteristics of a parent. The learned traits during this period exerts a long lasting effect on the development of the animal. In rare cases, animals can associate their parental figure with an animal of another species. Stories of animal X being raised by animal Y shows that this filial connection made by animal X is long lasting. Animal X will believe that animal Y, who they connected with as primary care giver, independent on species (Bolhuis et al., 1990). Sluckin (1972) even showed ducklings forming a filial connection with inanimate laboratory objects, in the absence of a proper parent.
Another example of a critical period is how certain song birds (Bengalese finch in this case) learn their mating call. These juvenile finches learn their mating call through listening to and repetition of an adult male’s call. The critical phase for a male to learn it’s mating call is 25-90 days of life (Woolley et al., 2002).If the call is not learned and mastered with in this time, the finch will never be able to master it. If the juvenile exclusively hears another species mating call, it will learn and repeat it. If the other species call is mastered and the critical period has closed, the finch will now sing a ‘wrong’ mating song for its lifespan, severely hindering it’s reproductive chances. Even prolonged exposure to the proper mating call will have no significant effect once the critical period has closed.
Mouse models demonstrate critical period plasticity during various times of
development (<P50) in nervous system based modalities such as the motor, visual, auditory, somatasensory, taste and olfactory senses (Hensch, 2004). Figure 4 below compiles known critical periods and the coinciding known regulator of the development per species. These critical periods of development are seen in both the periphery as well as the central nervous system. A clear model of synaptic plasticity in is seen within the prerequisite motor control critical period of development in mice. During the first 15 days of life, multiple motor axons compete for a single muscle fiber, resulting in the elimination of synapses at the neonatal neuromuscular junction (Sanes and Lichtman, 1999). Salvation or decay of the synapses is
modulated by the amount of neuromuscular activity (e.g. Thompson, 1985). Direct visualization of the removal of extra motor axons during the first postnatal weeks in rats demonstrates the progression of synaptic events reinforcing functional efficacy, eventually resulting in consolidation of its structure (Walsh & Lichtman 2003).
Affecting factors of plasticity
The natural cascade of mechanisms that activates plasticity is currently unknown. However, some elements have been pinned as essential in the plasticity process. Figure 5 below shows a compiled list of different techniques that have been show to activate non age-dependent plasticity. The plasticity inducing activities include sensory/motor experience, learning, psychoactive drugs, sex, stress and diet, as well as others. While all of these have been proven to factor in the synaptic organization of the brain, we do not fully understand cellular process that contributes to the architectural neuronal design.
Fig. 5- Factor affecting the synaptic organization of the normal brain. (Kolb and Gibb, 2013)
Functional Under-connectivity in ASD.
A multitude of studies have shown long range functional under-connectivity and short range functional over-connectivity in an average autistic person. Functional under-connectivity is when a network is functionally weaker and less efficient at transmitting information when compared with normative activation. Functional under-connectivity is seen in various parts of in an average person with autism and is hypothesized to contribute to the clinically expressed ASD symptoms, such as in the connectivity theory. The hypothesis is based on the idea that many complex cognitive processes are composed of large-scale cortical networks, consisting of spatially separate microcircuits, which are then incorporated into a central complex
cognitive process. Under-connectivity between spatially separated regions leads to an inability to properly incorporate the information that has been sub-processed with in separate regions into a compiled world view. Just et al., (2004) conducted a fMRI study looking at local and long range activation of people with autism compared to normative controls during a reading comprehension tasks. The study found more activation in Wernicke’s area in a person with autism, yet the control subjects showed more activation in Broca’s area. The Wernicke’s area is associated with the processing of words that we hear being spoken and Broca’s area is associated with production of language and language outputs. Broca’s area is also downstream from Wernicke’s area during auditory processing (Dubuc, 2002). Reduced activation in Broca’s area in people with autism can suggests a deficiency in information processing. While a person with autism may properly hear a language, the information may not be successfully transported to the Broca’s area for further auditory processing. The same
study found a decrease in functional connectivity between various anatomical regions of interest (ROI) in people with autism. Thirst-five cortical ROIs were defined for each participant using a conventional cortical parcellation scheme. The ROIs selected included the association areas most likely activated during a sentence comprehension task. 186 connections between these ROI’s were analyzed in order to determine connectivity between spatially separate regions. Ten regions showed significantly lower functional connectivity in a person with autism (see figure 6). Reduced connectivity between regions is seen in a person with autism on a wide range of cognitive tasks, such as visual attention, language, cognitive control and memory (e.g., Just et al., 2004, 2007; Mason et al., 2008; Solomon et al., 2009). Most abnormal connectivity was found between the frontal and parietal regions.(Just et al., 2007; Liu et al., 2011). Additionally, aberrant connectivity is seen among sub regions within the frontal cortex (e.g., Liu et al., 2011), such as with the anterior cingulated cortex and other frontal regions. (e.g., Kana et al.,2007). Brain activity within cortical networks, especially the frontal and parietal cortices, appears to be poorly coordinated in people with autism. In summation, the majority of studies suggest that the brain is characterized by long range under-connectivity and weakened structural connectivity between networks in people with autism, particularly between the frontal cortex and other regions.
Fig. 6 Significant difference in functional connectivity between autistic and control participants. Error bars represent standard error of the mean. L= Left; R= right; CALC= calcarine fissure; DLPFC= dorsolateral prefrontal cortex, FEF= frontal eye field, IES= inferior extrastriate; IFG= inferior frontal gyrus; IPL= inferior parietal lobe; IPS= intraparietal sulcus; IT= interior temporal; TRIA= triangularis; OP= occipital pole; SMFP= superior medial frontal paracingulate.
Structural under-connectivity
Structural under-connectivity has also been demonstrated throughout different modalities. Structural under-connectivity refers to a weaker neuronal architecture, characterized by less synaptic connections in a network. Some MRI structural analysis include diffusion tensor imaging (DTI) in determining structural connectivity of white matter tracks. DTI maps the fractional anisotropy (FA), which is the movement freedom of water (mostly) through neuronal organizations, reflecting the diameter and density of fibers, as well as myelination and macrostructral features. A high FA score (.95) indicates very defined neural tracts, associated with more connections, organization and superior information exchange. A low FA score (.1) indicates weak structural unity, associated with less organization of the neuronal circuit and a lesser exchange of information. Many studies implementing DTI, consistently find evidence of reduced FA among various brain regions in both children and adults with ASD when compared to controls (Anagnostou & Taylor, 2011; e.g. Cheung et al., 2009; Ke et al., 2009; Sundaram et al., 2008; Kumar et al., 2009; Keller et al., 2007; Lee et al., 2007). While the location of observed FA decrease have varied
(orbitofrontal, medial prefrontal, temporal lobe, corpus callosum, cingulate cortex, arcuate fasciculus, ILF, uncinated fasciculus, cerebellar outflow tracts, internal capsule), wide spread decreases in FA score suggest poorly organized white matter in people with autism.
Additionally, volumetric MRI studies display weaker connected frontal, parietal and temporal lobes (Herbert et al., 2004; Lee et al., 2007).
Post-mortem anatomical studies have also demonstrated long range
under-connectivity between other cortical regions in people with autism, specifically in minicolumns (Courchesne and Pierce, 2005,Cassanova et al., 2002). Minicolumns are core lines of neurons which ascend vertically between layers VI and II. This area of mostly linear cells is rich in unmyelinated axon fibers, synapses and dendritic arborizations (Sheldon, 1981). The core of a minicolumn contains the majority of the cells, with the spaces directly adjacent to the column (neuropil space) containing relatively sparse cellular structure. Post-mortem studies indicate a reduced width of minicolumns in children (mean age 12) with autism as compared with a typically developed control (Cassanova et al., 2002). The columns are significantly smaller, less dense and contain less peripheral neuropil space. The Cassanova group found abnormalities in all three regions examined; prefrontal cortex (area 9) and temporal lobe (area 21 and 22) . According to a recent study, abnormally small minicolumns and surrounding neuropil space is seen throughout the frontal cortex in children with autism as young as 3 (Buxhoeveden et al., 2004). Courchsne’s group proposed that excessive, disorganized and inadequate connectivity within the frontal lobes leads to poorly synchronized connectivity between the frontal cortex and other brain regions (Courchesne and Pierce, 2005).
Functional over-connectivity
In addition to long-range under connectivity, many studies report local functional over-connectivity(e.g. Wass, 2011; Vissers et al., 2012; Belmonte et al., 2004; Markram et al., 2007). Functional over-connectivity is when a network becomes more activated and has an increase in information processing. Shukla et al., (2010) conducted a study investigating the local connectivity in children with autism (mean age 13.7) using the regional homogeneity approach (fMRI technique to measure local connectivity). The study found an increase in local connectivity for bilateral middle temporal and right parahippocampal gyri in the participants with autism when compared to typically developed controls. In an fMRI auditory oddball task, children 10-15 years old, with high functioning autism (n=9) or Asperger’s syndrome (n=3), displayed stronger activation in the prefrontal and inferior parietal cortices when compared with typically developing controls (Gomot et al., 2008). The two regions of significantly more activation are known to be involved in selection attention. In accordance, children diagnosed with ASD had better performance in target detection (detecting a novel tone). The children with ASD detected novel targets faster than, but with similar accuracy to the controls. (Gomot et al., 2008). During a visual face/object recognition fMRI task, adults with autism showed increased functional activation in the dorsal medial prefrontal cortex and the dorsal anterior cingulate cortex when viewing faces, compared with typically developed controls (Ditcher et al., 2009).
However, these findings are controversial as other studies have also found local functional under-connectivity (e.g. Khan et al., 2013). A study implementing MEG nesting oscillations (employing phase-amplitude coupling (PAC)) in adolescents and young adult men with autism, exhibited a decrease in local connectivity during non-specific visual face viewing tasks (Khan et al., 2013). Khan et al., (2013) noted no differences in PAC-based functional connectivity during general tasks between people with autism and typically developing
controls, however, a specialized face viewing task elicited strong PAC in the controls not seen in the participants with autism. It should be noted that this is the first study to directly
document local functional under-connectivity in people with ASD.
Structural over-connectivity
The function of neuronal circuits is directed by the structural organization of many individual neurons. Structural over-connectivity refers to an increase in physical connections in or between networks. A meta-analysis compiling MRI and post-mortem studies concluded that structural over-connectivity is seen in the frontal lobes of children at the microscopic level in the means of excess neurogenesis, defective apoptosis, and migration defects
(Courchesne and Pierce, 2005). Courchesne and Pierce, (2005) hypothesize that the observed abnormal process results in malformation of the minicolumn microcircuity. A challenge with this structural connectivity is that it is difficult to properly measure structural local connectivity in a human. Therefore, animal models of autism have been produced in
order to further investigate aberrant structures in autism. Animal models can directly and quantitatively asses neuronal connectivity (post-sacrifice).
Much of the animal research done in autism implements the Valproic acid (VPA) rat model established by Rodier et al. (1997). A mother rat is injected with VPA during
embryogenesis. Offspring of pregnant VPA exposed rats show many of the anatomical and behavioral symptoms of autism. VPA offspring show anatomical and behavioral symptoms typical of a human with autism, including a diminished number of cerebellar Purkinje neurons, repetitive behaviors, impaired social interaction, enhanced fear memory processing, as well as others (Markram et al., 2008). Due to similarities between the VPA rat and humans with autism, this rat model has been extensively researched and confirmed for validity. The VPA offspring rats show the number of direct connections between layer 5 pyramidal cells was increased by more than 50% for both excitatory and inhibitory cells. However, these over-connected cell clusters have a reduced maximum synaptic output as well as weakened excitatory synaptic response (Rinialdi et al., 2008). Elucidating that while there may be more physical connections, the functional connections between the neurons are weakened. The amygdala of VPA rat offspring is shown to be over-plastic, over-reactive, and generates enhanced anxiety and fear processing (Markam et al., 2008). A histological study on Fmr1-KO1 mice (model for ASD and mental retardation) found clear local over-connectivity in the mPFC network during development (p=12-36) (Testa-Silva et al., 2011).
How Over and Under-connectivity Define ASD
Under-connectivity has been regarded as a key aspect in defining autism in such long-range based theories such as CCT and connectivity. Under and over-connectivity can be used in tandem to describe many of the phonotypical autistic symptoms. Impaired
hypoconnectivity in long range processing could hamper social interaction and
communications. Lack of functional connectivity between regions makes it difficult for various regions of the brain to coordinate one coherent idea, both of the external world and possibly their own inner thoughts. Just et al., (2004), demonstrated variation in activation of the Broca’s and Wernicke’s area in people with autism when compared with normative participants during a fMRI language comprehension task. The same study also observed lower functional connectivity in the group with autism between 10 reliable ROI’s. This varied activation, accompanied by subpar information processing, helps to explain reduced language and interpersonal skills. Typically during auditory language processing, the Wernicke’s area is activated when sounds are heard to associate the proper word. Then, the partially processed information is sent downstream to the Broca’s area, typically associated with the formation of speech. Improper communication between these two areas can explain much of the language deficits seen in people with autism. Under activation of Broca’s area can account for the lack of overall speech within the person. Additionally, Broca’s area is thought to be part of a mirror neuron like system, due to its homogeneity with macaque premotor area F5, which is known to contain a mirror neuron system (Rizzolatti, Fogassi, & Gallese, 2002). Conveying that if the
mirror neuron like system is not activated in a person with autism, they will not achieve the same informational input and processing as a typical developed person. Improper information transfer between the many different regions and aspects of language processing could result in an aberrant development.
Another key symptom of autism is the restricted repertoire of interests and repetitive behaviors. Functional over-connectivity in region can result in over activation for the
coinciding stimuli (Markram et al., 2007). Due to the lack of information processing between networks (under-connectivity) these networks of over-connectivity become isolated. When a particular stimulus frequently activates an over-connected region, the frequent over activation will strengthen the connections even more (experience-independent plasticity). Due to lack of long-range connectivity, the over-connected regions are not modulated by proper inhibitory inputs from other processing regions. Resulting in an abnormally strong response to the stimuli, causing this region to become highly attenuated to by the person with autism. As the person attunes to this stimuli more, the process will become more and more reinforced. Insufficient information transfer could isolate the network for its typical inhibitory network. Isolation of a network that is being frequently reinforced and strengthen could lead to a miss understanding/over reaction to the situation. Information processed in this area would become highly attenuated to (Markram et al., 2007). However, due to lack of functional connectivity between sensory and inhibitory networks, the information may be improperly analyzed. Improper analysis of a highly attenuated stimuli (due to over active isolated networks) could result in an aberrant response. Eventually leading to extinction of other traits and habituation of few traits.
How Abnormal Plasticity results in Under/Over-connectivity
It is the belief of this author that many of the symptoms seen in autistic people can be explained with under/over-connectivity and that under/over-connectivity can be explained by aberrant plasticity mainly during development. Premature activation of critical period like plasticity processes would result in a premature increase of neurons and glial cells; which can accounts for the increased head size seen in some children with autism. An increase in head size is seen in 6-14 months, well before the display of clinical behavioral symptoms
(Courchesne et al., 2003). Additionally, in general, the brains of children with autism
prematurely grow and become larger (volume, weight) than that of a typically developing child during 6-14 months of life. After this stage, a plateau of growth is seen in children with autism, yet an increase in neurological development is seen in typically developing children.
Suggesting the possibility that a critical period in plasticity was prematurely active in the child with autism. Premature activation (6-14 months) of a critical period plasticity process could result in aberrant development of the critical trait/s (such as language). During a premature increase in plasticity, neurons will begin attempting to form the functional structural bonds between regions, in order to acquire fundamental traits. However, since the entirety of the brain is relatively underdeveloped due to age, proper connections may not be formed
between networks. The trait to be mastered during this premature critical period will most likely not be mastered due to insufficiently developed support systems, resulting in a functionally weak network. This time (6-14 months) in a typically developing child’s life coincides with what is normally a period of exuberant synaptogenesis, ongoing myelination and dendritic arborization (Belmonte et al., 2004). By missing this period of neural
organization, an autistic child’s aberrant connections may not be properly discarded resulting in hyperconnectivity within an activated region. ‘When a developing brain is confronted with an abnormal constraint on information processing, it will evolve an abnormal organization in order to accommodate that constraint, resulting in a succession of autistic behavioral abnormalities extending into sensory, motor and later developing cognitive functions.’ (Belmonte et al., 2004b). If these autistic behaviors and other traits are learned during a critical period of plasticity and not corrected, they will never be ameliorated.
Solutions
A possible next step in understanding critical period plasticity would be an attempt to reopen critical period like plasticity. Some pharmaceuticals, such as SSRI’s and nerve growth factor, have shown to increase plasticity in adult humans (Castrein et al., 2012). Sertraline (a commonly prescribed SSRI) was administer to typically developing human adult controls in order to compare their results with depressed participants that are prescribed sertraline. Castrein et al., (2012) noticed the control subjects’ visual functional plasticity levels increasing after administration of the sertaline.
Application of an sertraline like SSRI in an animal model may help elucidate the functional effects of the increased plasticity. Additionally, the enzyme chondroitinase ABC (ChABC) has also been shown to reactivate-critical period like plasticity via the modification of perineuronal nets within the brain of adult rats (Castrein et al., 2012). Injection of ChABC disrupts perinuronal nets which surround mostly inhibitory interneurons and develop coinciding with the closure of a critical period. (Berardi et al., 2004). However, the invasive procedures accompanying ChABC application makes it currently unacceptable for human use. Therefore, it should be further investigated if application of pharmaceuticals can re-open critical period like plasticity to a functional extent in an animal model. If functional recovery is seen in animal models (such as VPA rats), then future testing could elucidate the use of the most effective pharmaceuticals for human testing.
Application of pharmaceuticals such as these could potentially cause a critical period like state of plasticity. SSRI’s have been given to children under the age of 18 in U.S and the Netherlands (Bhangoo et al., 2003). The SSRI Fluvoxamine has been clinically investigated in its use in children with autism. The small sample study (n=18) demonstrated significant improvements in the clinical assessments of autistic symptoms such as eye contact and language use (Fukuda et al., 2001). About half of the children also showed improvement on the Clinician Global Impression scale. Additionally, no severe of adverse side effects were
observed(Fukuda et al., 2001). Fluvoxamine treatment as also been used to treat anxiety and depression in children (Gothelf et al., 2005; Riddle et al., 2001). Resulting in the possibly application of SSRI therapy in people with autism
If a person’s neurological network re-enters a stage of early life like plasticity, they may be able to learn and restructure the improperly mastered traits that should have been learned during early development. By inducing a critical period like state, accompanied by a retraining program, a child with autism may be able to relearn many of the enduring traits previously learned during aberrant development.
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