Attention-deficit/hyperactivity disorder (ADHD) has long been conceptualized as a neurobiological disorder of the prefrontal cortex and its connections. Circuits with the prefrontal cortex relevant ...to ADHD include dorsal frontostriatal, orbitofronto-striatal, and fronto-cerebellar circuits. Dorsal frontostriatal circuitry has been linked to cognitive control, whereas orbitofronto-striatal loops have been related to reward processing. Fronto-cerebellar circuits have been implicated in timing. Neurobiological dysfunction in any of these circuits could lead to symptoms of ADHD, as behavioral control could be disturbed by: 1) deficits in the prefrontal cortex itself; or 2) problems in the circuits relaying information to the prefrontal cortex, leading to reduced signaling for control. This article suggests a model for differentiating between interlinked reciprocal circuits with the prefrontal cortex in ADHD. If such a differentiation can be achieved, it might permit a neurobiological subtyping of ADHD, perhaps by defining “dorsal fronto-striatal,” “orbitofronto-striatal,” or “fronto-cerebellar” subtypes of ADHD. This could be useful as a template for investigating the neurobiology of ADHD and, ultimately, clinically.
Developmental imaging studies show that cortical grey matter decreases in volume during childhood and adolescence. However, considerably less research has addressed the development of subcortical ...regions (caudate, putamen, pallidum, accumbens, thalamus, amygdala, hippocampus and the cerebellar cortex), in particular not in longitudinal designs. We used the automatic labeling procedure in FreeSurfer to estimate the developmental trajectories of the volume of these subcortical structures in 147 participants (age 7.0–24.3years old, 94 males; 53 females) of whom 53 participants were scanned twice or more. A total of 223 magnetic resonance imaging (MRI) scans (acquired at 1.5-T) were analyzed. Substantial diversity in the developmental trajectories was observed between the different subcortical gray matter structures: the volume of caudate, putamen and nucleus accumbens decreased with age, whereas the volume of hippocampus, amygdala, pallidum and cerebellum showed an inverted U-shaped developmental trajectory. The thalamus showed an initial small increase in volume followed by a slight decrease. All structures had a larger volume in males than females over the whole age range, except for the cerebellum that had a sexually dimorphic developmental trajectory. Thus, subcortical structures appear to not yet be fully developed in childhood, similar to the cerebral cortex, and continue to show maturational changes into adolescence. In addition, there is substantial heterogeneity between the developmental trajectories of these structures.
•Development of subcortical structures was examined in 150 typically developing children.•Similar to the cerebral cortex, subcortical structures are not yet fully developed in childhood.•There was substantial heterogeneity in developmental trajectories of subcortical structures.•All structures were larger in males than females over the whole age range.•The cerebellum was the only structure to show a sexually dimorphic trajectory.
Response inhibition involves proactive and reactive modes. Proactive inhibition is goal-directed, triggered by warning cues, and serves to restrain actions. Reactive inhibition is stimulus-driven, ...triggered by salient stop-signals, and used to stop actions completely. Functional MRI studies have identified brain regions that activate during proactive and reactive inhibition. It remains unclear how these brain regions operate in functional networks, and whether proactive and reactive inhibition depend on common networks, unique networks, or a combination. To address this we analyzed a large fMRI dataset (N=65) of a stop-signal task designed to measure proactive and reactive inhibition, using independent component analysis (ICA). We found 1) three frontal networks that were associated with both proactive and reactive inhibition, 2) one network in the superior parietal lobe, which also included dorsal premotor cortex and left putamen, that was specifically associated with proactive inhibition, and 3) two right-lateralized frontal and fronto-parietal networks, including the right inferior frontal gyrus and temporoparietal junction as well as a bilateral fronto-temporal network that were uniquely associated with reactive inhibition. Overlap between networks was observed in dorsolateral prefrontal and parietal cortices. Taken together, we offer a new perspective on the neural underpinnings of inhibitory control, by showing that proactive inhibition and reactive inhibition are supported by a group of common and unique networks that appear to integrate and interact in frontoparietal areas.
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•Proactive and reactive inhibitory controls are supported by multiple networks.•We found proactive (P), reactive (R), and proactive/reactive (PR) networks.•P and P/R networks are bilaterally organized; R network is right-dominant.•These networks may integrate and interact in dorsolateral frontoparietal areas.
This paper discusses how converging methods may form a powerful tool in unraveling the neurobiology of attention-deficit/hyperactivity disorder (ADHD). Integrating findings from multiple disciplines ...can inform us on how different neurobiological and cognitive mechanisms tie together in both typical and atypical development. Examples are discussed of this approach: combining family and genetic approaches with anatomical neuroimaging illustrates how mapping familial effects can bring us closer to understanding the neurobiology of ADHD. Functional neuroimaging has convincingly linked cognitive problems in this disorder with frontostriatal functioning, but also shows that other systems may be involved in some of the symptoms of ADHD. Combining these findings has suggested new avenues for investigation, such as the role of frontocerebellar networks. Furthermore, findings may have practical applications: this paper discusses an example of how converging evidence of striatal dysregulation in ADHD suggests possible directions for treatment that are now being explored in functional imaging studies.
Background Repetitive behavior is a core feature of autism and has been linked to differences in striatum. In addition, the brain changes associated with autism appear to vary with age. However, most ...studies investigating striatal differences in autism are cross-sectional, limiting inferences on development. In this study, we set out to 1) investigate striatal development in autism, using a longitudinal design; and 2) examine the relationship between striatal development and repetitive behavior. Methods We acquired longitudinal structural magnetic resonance imaging scans from 86 individuals (49 children with autism, 37 matched control subjects). Each individual was scanned twice, with a mean scan interval time of 2.4 years. Mean age was 9.9 years at time 1 and 12.3 years at time 2. Striatal structures were traced manually with high reliability. Multivariate analyses of variance were used to investigate differences in brain development between diagnostic groups. To examine the relationship with behavior, correlations between changes in brain volumes and clinical measures were calculated. Results Our results showed an increase in the growth rate of striatal structures for individuals with autism compared with control subjects. The effect was specific to caudate nucleus, where growth rate was doubled. Second, faster striatal growth was correlated with more severe repetitive behavior (insistence on sameness) at the preschool age. Conclusions This longitudinal study of brain development in autism confirms the involvement of striatum in repetitive behavior. Furthermore, it underscores the significance of brain development in autism, as the severity of repetitive behavior was related to striatal growth, rather than volume per se.
The way we understand the world we live in is changing. Our traditional understanding is being challenged by developments in physics, including quantum mechanics, and our inability to explain certain ...complex phenomena such as consciousness. In this book, scholars from a variety of backgrounds discuss how our understanding of our world is expanding to include such phenomena.
The neurobiology of repetitive behavior: …and men Langen, Marieke; Durston, Sarah; Kas, Martien J.H. ...
Neuroscience and biobehavioral reviews,
January 2011, 2011, 2011-Jan, 2011-01-00, 20110101, Letnik:
35, Številka:
3
Journal Article
Recenzirano
In young, typically developing children, repetitive behavior similar to that in certain neuropsychiatric syndromes is common. Whereas this behavior is adaptive in typical development, in many ...disorders it forms a core component of symptoms and causes prominent impairment in the daily life of affected individuals. Understanding the neurobiological mechanisms involved repetitive behavior will improve our understanding of the pathogenesis of developmental neuropsychiatric disorders, stimulating novel approaches to these conditions. However, studies on the neurobiology of human repetitive behavior have often been limited to distinct conditions and generalization has been hindered by inconsistent terminology. In this paper, we synthesize the ‘disorder-driven’ literature, building on findings from fundamental animal research and translational models. These findings suggest a model for classifying repetitive behavior by its neuroanatomical correlates.
Most studies aiming to predict transition to psychosis for individuals at ultra-high risk (UHR) have focused on either neurocognitive or clinical variables and have made little effort to combine the ...two. Furthermore, most have focused on a dichotomous measure of transition to psychosis rather than a continuous measure of functional outcome. We aimed to investigate the relative value of neurocognitive and clinical variables for predicting both transition to psychosis and functional outcome.
Forty-three UHR individuals and 47 controls completed an extensive clinical and neurocognitive assessment at baseline and participated in long-term follow-up approximately six years later. UHR adolescents who had converted to psychosis (UHR-P; n = 10) were compared to individuals who had not (UHR-NP; n = 33) and controls on clinical and neurocognitive variables. Regression analyses were performed to determine which baseline measures best predicted transition to psychosis and long-term functional outcome for UHR individuals.
Low IQ was the single neurocognitive parameter that discriminated UHR-P individuals from UHR-NP individuals and controls. The severity of attenuated positive symptoms was the only significant predictor of a transition to psychosis and disorganized symptoms were highly predictive of functional outcome.
Clinical measures are currently the most important vulnerability markers for long-term outcome in adolescents at imminent risk of psychosis.
Repetitive behaviour and inhibitory control deficits are core features of autism; and it has been suggested that they result from differences in the anatomy of striatum; and/or the ‘connectivity’ of ...subcortical regions to frontal cortex. There are few studies, however, that have measured the micro-structural organisation of white matter tracts connecting striatum and frontal cortex.
To investigate differences in bulk volume of striatum and micro-structural organisation of fronto-striatal white matter in people with autism; and their association with repetitive behaviour and inhibitory control.
We compared the bulk volume of striatum (caudate nucleus, putamen and nucleus accumbens) and white matter organisation of fronto-striatal tracts using (respectively) structural magnetic resonance imaging (sMRI) and tract specific diffusion tensor imaging (DTI) measures in 21 adults with autism and 22 controls. We also assessed performance on a cognitive inhibition (go/nogo) task.
Bulk volume of striatal structures did not differ between groups. However, adults with autism had a significantly smaller total brain white matter volume, lower fractional anisotropy of white matter tracts connecting putamen to frontal cortical areas, higher mean diffusivity of white matter tracts connecting accumbens to frontal cortex and worse performance on the go/nogo task. Also, performance on the go/nogo task was significantly related to anatomical variation when both groups were combined; but not within the autism group alone.
These data suggest that autism may be associated with differences in the anatomy of fronto-striatal white matter tracts.
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