Tim J. van Hartevelt

The functional human neuroanatomy of food pleasure cycles.

Food ensures our survival and is a potential source of pleasure and general well-being. In order to survive, the human brain is required to optimize the resource allocation such that rewards are pursued when relevant. This means that food intake follows a similar cyclical time course to other rewards with phases related to expectation, consummation and satiety. Here we develop a multilevel model for the full cycle of eating behavior based on the evidence for the brain networks and mechanisms initiating, sustaining and terminating the various phases of eating. We concentrate on how the underlying reward mechanisms of wanting, liking and learning lead to how human food intake is governed by both hedonic and homeostatic principles. We describe five of the main processing principles controlling food intake: hunger and attentional signal processing; motivation-independent discriminative processing; reward representations; learning-dependent multimodal sensory representations and hedonic experience. Overall, the evidence shows that while human food intake is complex, we are making progress in understanding the underlying mechanisms and that the brain networks supporting the food pleasure cycle are remarkably similar to those underlying the processing of other rewards.

Hypoactivation in Right Inferior Frontal Cortex is Specifically Associated With Motor Response Inhibition in Adult ADHD

Adult ADHD has been linked to impaired motor response inhibition and reduced associated activation in the right inferior frontal cortex (IFC). However, it is unclear whether abnormal inferior frontal activation in adult ADHD is specifically related to a response inhibition deficit or reflects a more general deficit in attentional processing. Using functional magnetic resonance imaging, we tested a group of 19 ADHD patients with no comorbidities and a group of 19 healthy control volunteers on a modified go/no‐go task that has been shown previously to distinguish between cortical responses related to response inhibition and attentional shifting. Relative to the healthy controls, ADHD patients showed increased commission errors and reduced activation in inferior frontal cortex during response inhibition. Crucially, this reduced activation was observed when controlling for attentional processing, suggesting that hypoactivation in right IFC in ADHD is specifically related to impaired response inhibition. The results are consistent with the notion of a selective neurocognitive deficit in response inhibition in adult ADHD associated with abnormal functional activation in the prefrontal cortex, whilst ruling out likely group differences in attentional orienting, arousal and motivation. Hum Brain Mapp 35:5141–5152, 2014.

Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks

The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. Using functional magnetic resonance imaging, it has been found that the large-scale resting-state functional connectivity (rsFC) in schizophrenia--measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal--exhibit altered network topology, with lower small-world index. The origin of these rsFC alterations and link with the underlying structural connectivity remain unclear. In this work, we used a computational model of spontaneous large-scale brain activity to explore the role of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. The structural connectomes from 15 adolescent patients with early-onset schizophrenia and 15 age- and gender-matched controls were built from diffusion tensor imaging data to detect the white matter tracts between 90 brain areas. Brain areas, simulated using a reduced dynamic mean-field model, receive excitatory input from other areas in proportion to the number of fibre tracts between them. The simulated mean field activity was transformed into BOLD signal, and the properties of the simulated functional networks were analyzed. Our results suggest that the functional alterations observed in schizophrenia are not directly linked to alterations in the structural topology. Instead, subtly randomized and less small-world functional networks appear when the brain operates with lower global coupling, which shifts the dynamics from the optimal healthy regime.

Evidence for a Caregiving Instinct: Rapid Differentiation of Infant from Adult Vocalizations Using Magnetoencephalography.

Crying is the most salient vocal signal of distress. The cries of a newborn infant alert adult listeners and often elicit caregiving behavior. For the parent, rapid responding to an infant in distress is an adaptive behavior, functioning to ensure offspring survival. The ability to react rapidly requires quick recognition and evaluation of stimuli followed by a co-ordinated motor response. Previous neuroimaging research has demonstrated early specialized activity in response to infant faces. Using magnetoencephalography, we found similarly early (100–200 ms) differences in neural responses to infant and adult cry vocalizations in auditory, emotional, and motor cortical brain regions. We propose that this early differential activity may help to rapidly identify infant cries and engage affective and motor neural circuitry to promote adaptive behavioral responding, before conscious awareness. These differences were observed in adults who were not parents, perhaps indicative of a universal brain-based “caregiving instinct.”

The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

Abstract In order to promote survival through flexible cognition and goal‐directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so‐called rich club which show dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole‐brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time (“dynamical workspace of binding nodes”). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone. HighlightsWe propose a novel method for finding the most functionally relevant brain regions for binding information over time.This “dynamical workspace of binding nodes” is determined using causal whole‐brain computational modelling.We compare the functional dynamical significance of binding nodes to the rich club members.Removal of binding nodes compared to rich club nodes significantly decreases integration.

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data.

ABSTRACT During rest, envelopes of band‐limited on‐going MEG signals co‐vary across the brain in consistent patterns, which have been related to resting‐state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole‐brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole‐brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12 Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio‐temporal structure of on‐going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large‐scale brain activity. HighlightsResting‐state MEG reveals correlated amplitude envelopes between brain areas.Envelope functional connectivity spans a range of carrier frequencies.Local emergence of carrier oscillations modeled with a Hopf bifurcation model.Each brain area may resonate at one or multiple fundamental frequencies.Multiple resonant frequencies outperform the single frequency scenario. Graphical abstract Figure. No Caption available.

Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome

Deep brain stimulation (DBS) is a remarkably effective clinical tool, used primarily for movement disorders. DBS relies on precise targeting of specific brain regions to rebalance the oscillatory behaviour of whole-brain neural networks. Traditionally, DBS targeting has been based upon animal models (such as MPTP for Parkinson’s disease) but has also been the result of serendipity during human lesional neurosurgery. There are, however, no good animal models of psychiatric disorders such as depression and schizophrenia, and progress in this area has been slow. In this paper, we use advanced tractography combined with whole-brain anatomical parcellation to provide a rational foundation for identifying the connectivity ‘fingerprint’ of existing, successful DBS targets. This knowledge can then be used pre-surgically and even potentially for the discovery of novel targets. First, using data from our recent case series of cingulate DBS for patients with treatment-resistant chronic pain, we demonstrate how to identify the structural ‘fingerprints’ of existing successful and unsuccessful DBS targets in terms of their connectivity to other brain regions, as defined by the whole-brain anatomical parcellation. Second, we use a number of different strategies to identify the successful fingerprints of structural connectivity across four patients with successful outcomes compared with two patients with unsuccessful outcomes. This fingerprinting method can potentially be used pre-surgically to account for a patient’s individual connectivity and identify the best DBS target. Ultimately, our novel fingerprinting method could be combined with advanced whole-brain computational modelling of the spontaneous dynamics arising from the structural changes in disease, to provide new insights and potentially new targets for hitherto impenetrable neuropsychiatric disorders.

Insights into brain architectures from the homological scaffolds of functional connectivity networks

In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brains functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brains complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

The neural mechanism of hedonic processing and judgment of pleasant odors: An activation likelihood estimation meta-analysis.

OBJECTIVE Pleasure is essential to normal healthy life. Olfaction, as 1 of the neurobehavioral probes of hedonic capacity, has a unique advantage compared to other sensory modalities. However, it is unclear how olfactory hedonic information is processed in the brain. This study aimed to investigate olfactory hedonic processing in the human brain. METHOD We conducted an activation likelihood estimation (ALE) meta-analysis on 16 functional imaging studies that examined brain activation in olfactory hedonic processing-related tasks in healthy adults. RESULTS The results show that there is a core olfactory hedonic processing network, which consists of the bilateral parahippocampal gyrus/amygdala (BA34), the left middle frontal gyrus (BA6), the right middle frontal gyrus/lateral orbitofrontal cortex (OFC; BA10), the bilateral cingulate gyrus (BA32), the right lentiform nucleus/lateral globus pallidus, the right medial frontal gyrus/medial OFC (BA11), the left superior frontal gyrus (BA10), and the right insula (BA13). Moreover, our findings highlight that the right hemisphere is predominant in explicit odor hedonic judgment. Finally, the results indicate that there are significant differences in brain activation for hedonic judgment and passive smelling. CONCLUSION These results support the hypothesis that the OFC plays a key role in explicit hedonic judgment. (PsycINFO Database Record

Disrupted connectivity in schizophrenia: modelling the impact of structural connectivity changes on the dynamics of spontaneous functional networks

The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. In particular, a number of studies have found significant alterations in large-scale resting-state functional connectivity (FC) in the disease. The origin of these FC alterations and its potential link with the underlying structure, remain unclear. The FC between brain areas during rest (measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal recorded with functional MRI (fMRI)), is known to be strongly shaped by the underlying structural connectivity. However, the relationship between anatomical and functional brain connectivity is not trivial and computational models of large-scale neural dynamics are unique tools to explore this relationship [1-3]. Importantly, models can be used to predict the effects of structural alterations on the large-scale brain dynamics [4,5], which is beyond reach on the experimental side. In this work, the structural connectomes from XX patients with schizophrenia and from XX age- and gender-matched controls were built from DTI data using advanced tractography algorithms to detect the white matter tracts between 90 brain areas. In the model, each brain area was represented by a pool of spiking neurons, and its activity was described by a dynamic mean field model. Each brain area -or node in the global network- receives excitatory input from structurally connected regions in proportion to the number of fibre tracts detected, which may vary from subject to subject. The large-scale spontaneous activity, simulated with the model using the different structural connectomes, was compared between patients and controls. We have found that, in schizophrenia, the coupling weights are weaker, which shifts the bifurcation point (above which the dynamics becomes unstable) to a higher global coupling weight. In addition, the simulated mean field activity was transformed into BOLD signal, and the properties of the simulated FCs were analyzed using measures from graph theory. Our results indicate that the subtle randomization of functional networks occurring in schizophrenia is related to alterations in the underlying structural connectivity, which shift the dynamical regime of the brain at rest further away from the bifurcation point, which may have an impact on the behavioural symptoms of schizophrenia.