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Surface Rendering of Brain Regions With Significant Activation in Healthy Comparison Subjects (N=8) and Patients With Autism (N=8) During Two Visuomotor Sequence Learning Task Experiments a
Source publication
Autism is a neurally based psychiatric disorder, but there is no consensus regarding the underlying neurofunctional abnormalities. Previous functional magnetic resonance imaging (fMRI) studies of simple movement suggested individually variable and scattered functional brain organization in autism. The authors examined whether such abnormalities gen...
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The precise localization of executive functions such as response inhibition within the prefrontal cortex (PFC), although theoretically crucial, has proven to be controversial and difficult¹. Functional neuroimaging has contributed importantly to this debate1, 2, 3, 4, 5, 6, ⁷, but as human cortical lesions are seldom discrete, the literature still...
This fMRI study investigates the neural mechanisms supporting the retrieval of action semantics. A novel motor imagery task was used in which participants were required to imagine planning actions with a familiar object (e.g. a toothbrush) or with an unfamiliar object (e.g. a pair of pliers) based on either goal-related information (i.e. where to m...
Citations
... Ten articles (Cole et al., 2019;Dinstein et al., 2010;Grèzes et al., 2009;Lepping et al., 2022;Marsh & Hamilton, 2011;Okamoto et al., 2014;Perkins et al., 2015;Poulin-Lord et al., 2014;Schütz et al., 2020;Unruh et al., 2019) were based on data collection on a 3T system, and six articles (Allen et al., 2004;Allen & Courchesne, 2003;Martineau et al., 2010;Müller et al., 2001;Müller et al., 2003;Müller et al., 2004) used a 1.5T system. Of the 16 records meeting the inclusion criteria, seven studies primarily investigated action execution (Allen et al., 2004;Allen & Courchesne, 2003;Lepping et al., 2022;Müller et al., 2001Müller et al., , 2003Müller et al., , 2004Unruh et al., 2019) and five primarily investigated action observation (Cole et al., 2019;Grèzes et al., 2009;Marsh & Hamilton, 2011;Perkins et al., 2015;Schütz et al., 2020). ...
... Ten articles (Cole et al., 2019;Dinstein et al., 2010;Grèzes et al., 2009;Lepping et al., 2022;Marsh & Hamilton, 2011;Okamoto et al., 2014;Perkins et al., 2015;Poulin-Lord et al., 2014;Schütz et al., 2020;Unruh et al., 2019) were based on data collection on a 3T system, and six articles (Allen et al., 2004;Allen & Courchesne, 2003;Martineau et al., 2010;Müller et al., 2001;Müller et al., 2003;Müller et al., 2004) used a 1.5T system. Of the 16 records meeting the inclusion criteria, seven studies primarily investigated action execution (Allen et al., 2004;Allen & Courchesne, 2003;Lepping et al., 2022;Müller et al., 2001Müller et al., , 2003Müller et al., , 2004Unruh et al., 2019) and five primarily investigated action observation (Cole et al., 2019;Grèzes et al., 2009;Marsh & Hamilton, 2011;Perkins et al., 2015;Schütz et al., 2020). Two studies focused on both action execution and observation (Dinstein et al., 2010;Martineau et al., 2010), and two studies focused on action imitation (Okamoto et al., 2014;Poulin-Lord et al., 2014). ...
... In total, the articles included in this review have been cited 1205 times (citations for each article are provided in Table 1). The majority of citations were generated from five studies (Allen et al., 2004;Allen & Courchesne, 2003;Müller et al., 2001Müller et al., , 2003Müller et al., , 2004, with 740 (61.4%) citations combined. ...
Lay Summary
Movement difficulties are common in autistic individuals, possibly at least partly due to problems with planning the required motor activities to achieve an action goal and monitoring this motor plan during action. Problems with performing actions in autism may also be related to problems with understanding the action of others. One way to increase the understanding of movement problems in autism, and the particularities that may be related to such problems, is to explore what goes on in the brain during action performance. In this article, we present an overview of studies from the last 20 years that have used functional brain imaging to investigate brain activity during execution, observation, and imitation of naturalistic actions (i.e., ordinary motor behavior in the physical world) in autistic adults compared with neurotypical adults. Summarizing the various study results, we found that autistic adults mainly showed brain activity in similar brain areas as neurotypical adults, although differing in magnitude and/or direction (higher or lower activity). This indicates atypical engagement of brain regions that are important for action‐related activities, such as movement planning and performance and accessing prior experience of movement in autistic individuals. Given the frequency of movement problems in autism, it is therefore of importance to continue investigating how these problems relate to brain activity. This will help to establish a reliable scientific basis for clinical recommendations and interventions, and potentially add to the understanding of autism in general.
... Current evidence surrounding this lateralization debate is inconsistent, with findings for generally increased activity in the right hemisphere in autism [14][15][16][17][18], generally decreased activity in the left hemisphere [19][20][21], both increased activity in the right hemisphere and decreased activity in the left hemisphere [22][23][24], and generally decreased connectivity across both hemispheres [25]. Conversely, recent evidence for specific differences in lateralization for regions involved in language processing in autism is compelling. ...
Background
Autism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.
Methods
In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.
Results
We found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.
Conclusions
These findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.
... For example, it would be interesting to know how contributions to network specialization may be different in autism, for which atypical functional lateralization patterns have been reliably observed (Anderson et al., 2010;Cardinale et al., 2013;Eyler et al., 2012;Harris et al., 2006;Jouravlev et al., 2020;Kleinhans et al., 2008;X.-Z. Kong et al., 2022;Lindell & Hudry, 2013;Müller et al., 2003;Nielsen et al., 2014;Persichetti et al., 2022;Redcay & Courchesne, 2008) as well as schizophrenia (Agcaoglu et al., 2018;Ocklenburg et al., 2013;Sommer et al., 2001). ...
One organizing principle of the human brain is hemispheric specialization, or the dominance of a specific function or cognitive process in one hemisphere or the other. Previously, Wang et al. (2014) identified networks putatively associated with language and attention as being specialized to the left and right hemispheres, respectively; and a dual-specialization of the executive control network. However, it remains unknown which networks are specialized when specialization is examined within individuals using a higher resolution parcellation, as well as which connections are contributing the most to a given network’s specialization. In the present study, we estimated network specialization across three datasets using the autonomy index and a novel method of deconstructing network specialization. After examining the reliability of these methods as implemented on an individual level, we addressed two hypotheses. First, we hypothesized that the most specialized networks would include those associated with language, visuospatial attention, and executive control. Second, we hypothesized that within-network contributions to specialization would follow a within-between network gradient or a specialization gradient. We found that the majority of networks exhibited greater within-hemisphere connectivity than between-hemisphere connectivity. Among the most specialized networks were networks associated with language, attention, and executive control. Additionally, we found that the greatest network contributions were within-network, followed by those from specialized networks.
Significance Statement
Hemispheric specialization is a characteristic of brain organization that describes when a function draws on one hemisphere of the brain more than the other. We sought to identify the most specialized brain networks within individuals, as well as which connections contribute the most to a given network’s specialization. Among the most specialized networks were those associated with language, attention, and executive control. Unexpectedly, we also identified networks associated with emotion/memory and theory of mind as highly specialized. Additionally, we found support for guiding principles of brain organization generally, such that within-network connections contributed most to a given network’s specialization followed by connections from other specialized networks. These results have implications for identifying potential variations of network contributions in individuals with neurodevelopmental conditions.
... This work adds to the growing body of literature emphasizing the importance of understanding intra-subject neural variability in individuals with ASD, as the sole focus on average group values may mask these more nuanced differences (David et al., 2016;Dinstein et al., 2012;Haigh, 2018;Haigh, Heeger, Dinstein, Minshew, & Behrmann, 2015;Hawco et al., 2020;Magnuson, Iarocci, Doesburg, & Moreno, 2020;Milne, 2011;Müller, Kleinhans, Nobuko Kemmotsu, Karen Pierce, & Courchesne, 2003;Pierce, Müller, Ambrose, Allen, & Courchesne, 2001;Poulin-Lord et al., 2014;Simmons et al., 2009;Vilidaite, Yu, & Baker, 2017;Weinger, Zemon, Soorya, & Gordon, 2014). For example, individuals with ASD had increased neural variability in both sensory (visual, auditory somatosensory; Dinstein et al., 2012;Haigh et al., 2015Haigh et al., , 2016Kovarski et al., 2019;Magnuson et al., 2020;Milne, 2011;Weinger et al., 2014;Yu, Wang, Huang, Wu, & Zhang, 2018) and motor tasks (Dinstein et al., 2010;Müller et al., 2003). ...
... This work adds to the growing body of literature emphasizing the importance of understanding intra-subject neural variability in individuals with ASD, as the sole focus on average group values may mask these more nuanced differences (David et al., 2016;Dinstein et al., 2012;Haigh, 2018;Haigh, Heeger, Dinstein, Minshew, & Behrmann, 2015;Hawco et al., 2020;Magnuson, Iarocci, Doesburg, & Moreno, 2020;Milne, 2011;Müller, Kleinhans, Nobuko Kemmotsu, Karen Pierce, & Courchesne, 2003;Pierce, Müller, Ambrose, Allen, & Courchesne, 2001;Poulin-Lord et al., 2014;Simmons et al., 2009;Vilidaite, Yu, & Baker, 2017;Weinger, Zemon, Soorya, & Gordon, 2014). For example, individuals with ASD had increased neural variability in both sensory (visual, auditory somatosensory; Dinstein et al., 2012;Haigh et al., 2015Haigh et al., , 2016Kovarski et al., 2019;Magnuson et al., 2020;Milne, 2011;Weinger et al., 2014;Yu, Wang, Huang, Wu, & Zhang, 2018) and motor tasks (Dinstein et al., 2010;Müller et al., 2003). To date, only a few studies have reported relationships between intra-subject neural variability and autism severity. ...
Background
Communication difficulties are a core deficit in many people with autism spectrum disorder (ASD). The current study evaluated neural activation in participants with ASD and neurotypical (NT) controls during a speech production task.
Methods
Neural activities of participants with ASD (N = 15, M = 16.7 years, language abilities ranged from low verbal abilities to verbally fluent) and NT controls (N = 12, M = 17.1 years) was examined using functional magnetic resonance imaging with a sparse-sampling paradigm.
Results
There were no differences between the ASD and NT groups in average speech activation or inter-subject run-to-run variability in speech activation. Intra-subject run-to-run neural variability was greater in the ASD group and was positively correlated with autism severity in cortical areas associated with speech.
Conclusions
These findings highlight the importance of understanding intra-subject neural variability in participants with ASD.
... Therefore, motor pathways are not properly organized in subjects with ASD, leading to difficulties in generating appropriate motor responses [69]. In particular some functional imaging studies showed an atypical activation on the premotor cortex [70] and the cerebellum [71][72][73], during motor execution and decreased connectivity of the motor execution network [73]. Cardinale and colleagues reported atypical rightward lateralization of multiple functional brain networks in subjects with ASD, including language, motor and visuospatial circuits [74]. ...
Early attentional dysfunction is one of the most consistent findings in autism spectrum disorder (ASD), including the high functioning autism (HFA). There are no studies that assess how the atypical attentional processes affect the motor functioning in HFA. In this study, we evaluated attentional and motor functioning in a sample of 15 drug-naive patients with HFA and 15 healthy children (HC), and possible link between attentional dysfunction and motor impairment in HFA. Compared to HC, HFA group was seriously impaired in a considerable number of attentional processes and showed a greater number of motor abnormalities. Significant correlations between attention deficits and motor abnormalities were observed in HFA group. These preliminary findings suggest that deficit of attentional processes can be implied in motor abnormalities in HFA.
... The task evaluates the subjects by measuring their percentages of correct responses and omission errors and their average response time and its variability. Children with ASD tend to perform worse 9 and have high response time variability 10 than typical children during the task, which may be caused by variability in neural activations 11 . ...
Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker’s face during a conversation. To date, however, there has not been an adequate study examining children’s response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children’s gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children’s gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.
... Individuals with ASD also showed greater activity in frontal, striatal, and cerebellar regions suggesting increased involvement of cognitive control networks. Finger tapping studies have also documented aberrant frontal, parietal and cerebellar activity in ASD (Allen & Courchesne, 2003;Allen, Muller, & Courchesne, 2004;Mostofsky et al., 2009;Muller, Kleinhans, Kemmotsu, Pierce, & Courchesne, 2003;Muller, Pierce, Ambrose, Allen, & Courchesne, 2001). In the one known study to assess functional connectivity during motor behavior, Mostofsky et al. (2009) documented reduced cerebellar-thalamo-cortical functional connectivity alongside increased supplementary motor area activation and reduced cerebellar activation in ASD during finger tapping. ...
Sensorimotor abnormalities are common in autism spectrum disorder (ASD) and predictive of functional outcomes, though their neural underpinnings remain poorly understood. Using functional magnetic resonance imaging, we examined both brain activation and functional connectivity during visuomotor behavior in 27 individuals with ASD and 30 typically developing (TD) controls (ages 9–35 years). Participants maintained a constant grip force while receiving visual feedback at three different visual gain levels. Relative to controls, ASD participants showed increased force variability, especially at high gain, and reduced entropy. Brain activation was greater in individuals with ASD than controls in supplementary motor area, bilateral superior parietal lobules, and contralateral middle frontal gyrus at high gain. During motor action, functional connectivity was reduced between parietal-premotor and parietal-putamen in individuals with ASD compared to controls. Individuals with ASD also showed greater age-associated increases in functional connectivity between cerebellum and visual, motor, and prefrontal cortical areas relative to controls. These results indicate that visuomotor deficits in ASD are associated with atypical activation and functional connectivity of posterior parietal, premotor, and striatal circuits involved in translating sensory feedback information into precision motor behaviors, and that functional connectivity of cerebellar–cortical sensorimotor and nonsensorimotor networks show delayed maturation.
... The task evaluates the subjects by measuring their percentages of correct responses and omission errors and their average response time and its variability. Children with ASD tend to perform worse 9 and have high response time variability 10 than typical children during the task, which may be caused by variability in neural activations 11 . ...
Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker's face during a conversation. To date, however, there has not been an adequate study examining children's response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children's gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children's gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.
... In ASD, a variety of brain architectures are altered, ranging from the brain stem to the cerebellum and cerebral cortex [87][88][89][90]. In particular, the connectivity deficit in the parieto-frontal circuit involved in the mirror mechanism has been proposed to underlie some cognitive aspects of ASD [91,92]. ...
Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.
... The domain level sFNR abnormalities in ASD are mainly associated with sensorimotor and cerebellar domains. The increased cerebral activity but decreased cerebellar activity in ASD has been documented in literature (Mostofsky et al., 2009;Müller, Kleinhans, Kemmotsu, Pierce, & Courchesne, 2003). One possible explanation of such cerebralcerebellar dissociation can be the underconnectivity between those brain regions (Mostofsky et al., 2009). ...
The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step-wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole-brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ-ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning-problem-solving performance in SZ (r = -.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders.
