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Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD.
Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD.
Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age).
Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD.
Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.
Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities.
We combined two novel approaches. First, normative modeling to map deviations from the typical age-related pattern at the level of the individual of (i) activity during emotion matching and (ii) of anatomical images derived from DBD cases (n = 77) and controls (n = 52) aged 8–18 years from the EU-funded Aggressotype and MATRICS consortia. Second, linked independent component analysis to integrate subject-specific deviations from both modalities.
While cases exhibited on average a higher activity than would be expected for their age during face processing in regions such as the amygdala when compared to controls these positive deviations were widespread at the individual level. A multimodal integration of all functional and anatomical deviations explained 23% of the variance in the clinical DBD phenotype. Most notably, the top marker, encompassing the default mode network (DMN) and subcortical regions such as the amygdala and the striatum, was related to aggression across the whole sample.
Overall increased age-related deviations in the amygdala in DBD suggest a maturational delay, which has to be further validated in future studies. Further, the integration of individual deviation patterns from multiple imaging modalities allowed to dissect some of the heterogeneity of DBD and identified the DMN, the striatum and the amygdala as neural signatures that were associated with aggression.
Brain imaging studies have shown altered amygdala activity during emotion processing in children and adolescents with oppositional defiant disorder (ODD) and conduct disorder (CD) compared to typically developing children and adolescents (TD). Here we aimed to assess whether aggression-related subtypes (reactive and proactive aggression) and callous-unemotional (CU) traits predicted variation in amygdala activity and skin conductance (SC) response during emotion processing.
We included 177 participants (n = 108 cases with disruptive behaviour and/or ODD/CD and n = 69 TD), aged 8–18 years, across nine sites in Europe, as part of the EU Aggressotype and MATRICS projects. All participants performed an emotional face-matching functional magnetic resonance imaging task.
Differences between cases and TD in affective processing, as well as specificity of activation patterns for aggression subtypes and CU traits, were assessed. Simultaneous SC recordings were acquired in a subsample (n = 63). Cases compared to TDs showed higher amygdala activity in response to negative faces (fearful and angry) v. shapes. Subtyping cases according to aggression-related subtypes did not significantly influence on amygdala activity; while stratification based on CU traits was more sensitive and revealed decreased amygdala activity in the high CU group. SC responses were significantly lower in cases and negatively correlated with CU traits, reactive and proactive aggression.
Our results showed differences in amygdala activity and SC responses to emotional faces between cases with ODD/CD and TD, while CU traits moderate both central (amygdala) and peripheral (SC) responses. Our insights regarding subtypes and trait-specific aggression could be used for improved diagnostics and personalized treatment.
Preterm birth is associated with an increased risk for cognitive-neurophysiological impairments and attention-deficit/hyperactivity disorder (ADHD). Whether the associations are due to the preterm birth insult per se, or due to other risk factors that characterise families with preterm-born children, is largely unknown.
We employed a within-sibling comparison design, using cognitive-performance and event-related potential (ERP) measures from 104 preterm-born adolescents and 104 of their term-born siblings. Analyses focused on ADHD symptoms and cognitive and ERP measures from a cued continuous performance test, an arrow flanker task and a reaction time task.
Within-sibling analyses showed that preterm birth was significantly associated with increased ADHD symptoms (β = 0.32, p = 0.01, 95% CI 0.05 to 0.58) and specific cognitive-ERP impairments, such as IQ (β = −0.20, p = 0.02, 95% CI −0.40 to −0.01), preparation-vigilance measures and measures of error processing (ranging from β = 0.71, −0.35). There was a negligible within-sibling association between preterm birth with executive control measures of inhibition (NoGo-P3, β = −0.07, p = 0.45, 95% CI −0.33 to 0.15) or verbal working memory (digit span backward, β = −0.05, p = 0.63, 95% CI −0.30 to 0.18).
Our results suggest that the relationship between preterm birth with ADHD symptoms and specific cognitive-neurophysiological impairments (IQ, preparation-vigilance and error processing) is independent of family-level risk and consistent with a causal inference. In contrast, our results suggest that previously observed associations between preterm birth with executive control processes of inhibition and working memory are instead linked to background characteristics of families with a preterm-born child rather than preterm birth insult per se. These findings suggest that interventions need to target both preterm-birth specific and family-level risk factors.
Autism spectrum disorder (ASD) and obsessive–compulsive disorder (OCD) are neurodevelopmental disorders with considerable overlap in terms of their defining symptoms of compulsivity/repetitive behaviour. Little is known about the extent to which ASD and OCD have common versus distinct neural correlates of compulsivity. Previous research points to potentially common dysfunction in frontostriatal connectivity, but direct comparisons in one study are lacking. Here, we assessed frontostriatal resting-state functional connectivity in youth with ASD or OCD, and healthy controls. In addition, we applied a cross-disorder approach to examine whether repetitive behaviour across ASD and OCD has common neural substrates.
A sample of 78 children and adolescents aged 8–16 years was used (ASD n = 24; OCD n = 25; healthy controls n = 29), originating from the multicentre study COMPULS. We tested whether diagnostic group, repetitive behaviour (measured with the Repetitive Behavior Scale-Revised) or their interaction was associated with resting-state functional connectivity of striatal seed regions.
No diagnosis-specific differences were detected. The cross-disorder analysis, on the other hand, showed that increased functional connectivity between the left nucleus accumbens (NAcc) and a cluster in the right premotor cortex/middle frontal gyrus was related to more severe symptoms of repetitive behaviour.
We demonstrate the fruitfulness of applying a cross-disorder approach to investigate the neural underpinnings of compulsivity/repetitive behaviour, by revealing a shared alteration in functional connectivity in ASD and OCD. We argue that this alteration might reflect aberrant reward or motivational processing of the NAcc with excessive connectivity to the premotor cortex implementing learned action patterns.
Attention-deficit hyperactivity disorder (ADHD) persists in around two-thirds of individuals in adolescence and early adulthood.
To examine the cognitive and neurophysiological processes underlying the persistence or remission of ADHD.
Follow-up data were obtained from 110 young people with childhood ADHD and 169 controls on cognitive, electroencephalogram frequency, event-related potential (ERP) and actigraph movement measures after 6 years.
ADHD persisters differed from remitters on preparation-vigilance measures (contingent negative variation, delta activity, reaction time variability and omission errors), IQ and actigraph count, but not on executive control measures of inhibition or working memory (nogo-P3 amplitudes, commission errors and digit span backwards).
Preparation-vigilance measures were markers of remission, improving concurrently with ADHD symptoms, whereas executive control measures were not sensitive to ADHD persistence/remission. For IQ, the present and previous results combined suggest a role in moderating ADHD outcome. These findings fit with previously identified aetiological separation of the cognitive impairments in ADHD. The strongest candidates for the development of non-pharmacological interventions involving cognitive training and neurofeedback are the preparation-vigilance processes that were markers of ADHD remission.
Integrating evidence from different imaging modalities is important to overcome specific limitations of any given imaging method, such as insensitivity of the EEG to unsynchronized neural events, or the lack of fMRI sensitivity to events of low metabolic demand. Processes that are visible in one modality may be related in a nontrivial way to other processes visible in another modality and insight may only be obtained by integrating both methods through a common analysis. For example, brain activity at rest seems to be at least partly determined by an interaction of cortical rhythms (visible to EEG but not to fMRI) with sub-cortical activity (visible to fMRI, but usually not to EEG without averaging). A combination of EEG and fMRI data during rest may thus be more informative than the sum of two separate analyses in both modalities.
Integration is also an important source of converging evidence about specific aspects and general principles of neural functions and their dysfunctions in certain pathologies. This is because not only electrical, but also energetic, biochemical, hemodynamic and metabolic processes characterize neural states and functions, and because brain structure provides crucial constraints upon neural functions. Focusing on multimodal integration of functional data should not distract from the privileged status of the electric field as the primary direct, noninvasive real-time measure of neural transmission.
The preceding chapters illustrate how electrical neuroimaging has turned scalp EEG into an imaging modality which directly captures the full temporal dynamics of neural activity in the brain.
Electrical neuroimaging is based on the analysis of brain electrical activity recorded from the human scalp with multichannel EEG. It offers enormous potential for the dynamic mapping of brain functions, and for the non-invasive diagnosis of neurological and psychiatric conditions. This authoritative reference gives a systematic overview of new electrical imaging methods, with a sound introduction to the basics of multichannel recording of EEG and event-related potential (ERP) data, as well as spatio-temporal analysis of the potential fields. The book enables researchers to measure valid data, select and apply appropriate analysis strategies, and avoid the most common mistakes when analyzing and interpreting EEG/ERP data. Importantly, it informs the research communities of the possibilities opened by these space-domain oriented approaches to the analysis of brain electrical activity, and of their potential to offer even more powerful diagnostic techniques when integrated with other clinically relevant data.
The EEG, along with its event-related aspects, reflects the immediate mass action of neural networks from a wide range of brain systems, and thus provides a particularly direct and integrative noninvasive window onto human brain function. During the 80 years since the discovery of the human scalp EEG, our neurophysiological understanding of electrical brain activity has advanced at the microscopic and macroscopic level and has been linked to physical principles, as summarized in standard textbooks. The present introduction builds upon these texts but focuses on spatial aspects of EEG generators, many of which are applicable to both spontaneous and event-related activity. In particular, it is critical for the purpose of electrical neuroimaging to know which neural events are detectable at which spatial scales. As we will show, the spatial characterization of the neural EEG generators, and the advances in spatial signal processing and modeling converge in important aspects and provide a sufficiently sound basis for electrical neuroimaging. Because of the unique high temporal resolution of the EEG, electrical neuroimaging not only concerns the possible neuronal generator of the scalp potential at one given moment in time, but also the possible generators of rhythmic oscillations in different frequency ranges. In fact, understanding the intrinsic rhythmic properties of cortical or subcortical–cortical networks can help to constrain electrical neuroimaging to certain frequency ranges of interest and to perform spatial analysis in the frequency domain.
A publication entitled “A default mode of brain function” initiated a new way of looking at functional imaging data. In this PET study the authors discussed the often-observed consistent decrease of brain activation in a variety of tasks as compared with the baseline. They suggested that this deactivation is due to a task-induced suspension of a default mode of brain function that is active during rest, i.e. that there exists intrinsic well-organized brain activity during rest in several distinct brain regions. This suggestion led to a large number of imaging studies on the resting state of the brain and to the conclusion that the study of this intrinsic activity is crucial for understanding how the brain works.
The fact that the brain is active during rest has been well known from a variety of EEG recordings for a very long time. Different states of the brain in the sleep–wake continuum are characterized by typical patterns of spontaneous oscillations in different frequency ranges and in different brain regions. Best studied are the evolving states during the different sleep stages, but characteristic EEG oscillation patterns have also been well described during awake periods (see Chapter 1 for details). A highly recommended comprehensive review on the brain's default state defined by oscillatory electrical brain activities is provided in the recent book by György Buzsaki, showing how these states can be measured by electrophysiological procedures at the global brain level as well as at the local cellular level.
The raw data for electrical neuroimaging is the potential field recorded on the scalp using multichannel EEG systems. Unlike waveform analysis of EEG or evoked potential (EP), electrical neuroimaging is based on the spatial analysis of these potential maps. The quality of these maps determines the goodness of the subsequent analysis steps. It is therefore of crucial importance that these scalp potential fields are recorded and pre-processed in a correct manner. An important issue concerns the number and the distribution of the electrodes on the scalp to provide an adequate spatial sampling of the potential field. Another issue is the measurement of the exact position of each electrode and the spatial normalization of the potential fields when averaging over subjects. Artifact detection and elimination is also an important point, since noise crucially influences source analysis. On the other hand, some factors that pose important problems for the traditional waveform analysis are irrelevant for electric neuroimaging. Obviously the selection of the “correct montage” for EEG analysis, or the “correct electrode” for evoked potential analysis is not relevant when analyzing the potential field. However, most important is that the question of the correct reference is completely obsolete for electrical neuroimaging. This fact was unfortunately often ignored and the “reference-problem” has been considered as a major disadvantage of the EEG compared with the MEG.
In 1929, Hans Berger, the founding father of electroencephalography (EEG), described EEG as a “window into the brain,” because EEG appeared to be a sensitive indicator of mental states. Eighty years later, recording and analysis methods exist that have made EEG a widespread and validated tool to observe the spatial and temporal dynamics of brain network activity during a large variety of mental states and processes in a completely noninvasive fashion. This has been made possible by significant technological advances that now allow the simultaneous recording of an EEG from a large number of electrodes at a high sampling rate, and the application of space-domain oriented approaches to the analysis of these recordings. This book gives an overview of these methods. Illustrated by various examples from experimental and clinical studies, the book is a tutorial on how to use EEG as a modern functional imaging method with the advantage of directly recording neuronal activity with millisecond temporal resolution, an approach called electrical neuroimaging.
Electrical neuroimaging has enormous potential if properly applied, but it can also easily lead to erroneous conclusions if its basic principles are not properly understood. This book intends to give a comprehensive introduction to the basics of multichannel recording of EEG and event-related potential (ERP) data, and to spatio-temporal analysis of the potential fields. All chapters include practical examples from clinical and experimental research.
In order to try to understand “how the brain works,” one must make measurements of brain function. And ideally, the measurements should be as noninvasive as possible, i.e. the brain should be disturbed as little as possible during the measurement of its functions. One of the first types of noninvasive measurements reported in the literature, by Hans Berger, that directly tapped brain function was the human electroencephalogram (EEG), consisting of scalp electric potential differences as a function of time. In fact, Berger saw the EEG as a “window into the brain.” One of Berger's first observations that showed compelling evidence of having tapped brain function was the alpha rhythm. This oscillatory activity, at around 10–12 Hz, is optimally recorded from a posterior electrode with an anterior reference. The activity is very pronounced when the human subject is with eyes closed, awake, alert, resting. By simply being instructed to perform a mental task such as overtly subtracting the number seven serially, starting at 500, the alpha activity disorganizes and almost disappears.
The main subject matter addressed in this chapter is the use of noninvasive extracranial measurements, i.e. the EEG and the magnetoencephalogram (MEG), for the estimation of the distribution in the brain of their electric neuronal generators. This can be seen as an extension of Berger's initial efforts towards developing a window into the brain.
Interhemispheric interactions were studied with
functional brain mapping of visual processing. Children
performed a reaction time task with uni- and bilateral
targets and nontargets. The visual evoked potential (VEP)
was segmented into P1a, P1b, and N1 microstates using map
rather than channel features. Map latencies, amplitudes
and sources were tested for bilateral interactions. Bilateral
targets yielded shorter VEP map latencies but later response
onsets than unilateral ones. Source analyses of the unilateral
VEPs indicated a transition from contra- (P1a) to ipsilateral
(P1b) visual cortex activation (interhemispheric transfer).
Bilateral VEPs were smaller than the summed unilateral
VEPs in all microstates, indicating that interhemispheric
interactions both precede and follow interhemispheric transfer.
Brain mapping of uni- and bilateral VEPs in children thus
revealed several distinct forms of interhemispheric interactions
in the same, early time range.
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