Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

Low-intensity ultrasound stimulation modulates cortical neurovascular coupling in an attention deficit hyperactivity disorder rat model

Attention deficit hyperactivity disorder is accompanied by changes in cranial nerve function and cerebral blood flow (CBF). Low-intensity ultrasound stimulation can modulate brain neural activity in attention deficit hyperactivity disorder. However, to date, the modulatory effects of low-intensity ultrasound stimulation on CBF and neurovascular coupling in attention deficit hyperactivity disorder have not been reported. To address this question, Sprague-Dawley, Wistar-Kyoto, and spontaneously hypertensive (attention deficit hyperactivity disorder (ADHD) rat model) rats were divided into the control and low-intensity ultrasound stimulation (LIUS) groups. Cortical electrical stimulation was used to induce cortical excitability in different types of rats, and a penetrable laser speckle contrast imaging (LSCI) system and electrodes were used to evaluate the electrical stimulation-induced CBF, cortical excitability, and neurovascular coupling in free-moving rats. The CBF, cortical excitability, and neurovascular coupling (NVC) under cortical electrical stimulation in the attention deficit hyperactivity disorder rats were significantly different from those in the Sprague-Dawley and Wistar-Kyoto rats. We also found that low-intensity ultrasound stimulation significantly interfered with the cortical excitability and neurovascular coupling induced by cortical electrical stimulation in rats with attention deficit hyperactivity disorder. Our findings suggest that neurovascular coupling is a potential biomarker for attention deficit hyperactivity disorder. Furthermore, low-intensity ultrasound stimulation can improve abnormal brain function in attention deficit hyperactivity disorder and lay a research foundation for its application in the clinical treatment of attention deficit hyperactivity disorder.

A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling

We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.

Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders

Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.

Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs

Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.

Investigation of phase synchronization in functional brain networks of children with ADHD using nonlinear recurrence measure

Measuring the phase synchronization between different brain regions in functional brain networks is a common approach to investigate many psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD). The emotional processing deficit in ADHD children is one of the main obstacles in their social interactions. In this study, the nonlinear Correlation between Probability of Recurrences (CPR) method is used for the first time to construct functional brain networks of 22 boys with ADHD and 22 healthy ones during watching four visual-emotional stimuli types. Topological features of brain networks, including shortest path length, clustering coefficient, and nodes strengths, are investigated in groups of ADHD and healthy. The results indicate a significantly (P-Values < 0.01) greater average clustering coefficient and lower shortest path length in the brain networks of ADHD individuals than the healthy ones. Accordingly, in the ADHD brain networks, the information exchange in both local and global scales is abnormally more than the healthy ones, leading to a hyper-synchronization in this group. The topological alterations of ADHD brain networks are mainly observed in the brain's frontal and occipital lobes, indicating impaired brain function of this group in emotional and visual processing. This survey demonstrates that the CPR method can be a good candidate for distinguishing the phase interactions of ADHD and healthy brain networks. Therefore, this study can contribute to further insights into the nonlinear dynamics analysis of brain networks in ADHD individuals.

Macroscopic resting state model predicts theta burst stimulation response: A randomized trial

Repetitive transcranial magnetic stimulation (rTMS) is a promising alternative therapy for treatment-resistant depression, although its limited remission rate indicates room for improvement. As depression is a phenomenological construction, the biological heterogeneity within this syndrome needs to be considered to improve the existing therapies. Whole-brain modeling provides an integrative multi-modal framework for capturing disease heterogeneity in a holistic manner. Computational modelling combined with probabilistic nonparametric fitting was applied to the resting-state fMRI data from 42 patients (21 women), to parametrize baseline brain dynamics in depression. All patients were randomly assigned to two treatment groups, namely active (i.e., rTMS, n = 22) or sham (n = 20). The active treatment group received rTMS treatment with an accelerated intermittent theta burst protocol over the dorsomedial prefrontal cortex. The sham treatment group underwent the identical procedure but with the magnetically shielded side of the coil. We stratified the depression sample into distinct covert subtypes based on their baseline attractor dynamics captured by different model parameters. Notably, the two detected depression subtypes exhibited different phenotypic behaviors at baseline. Our stratification could predict the diverse response to the active treatment that could not be explained by the sham treatment. Critically, we further found that one group exhibited more distinct improvement in certain affective and negative symptoms. The subgroup of patients with higher responsiveness to treatment exhibited blunted frequency dynamics for intrinsic activity at baseline, as indexed by lower global metastability and synchrony. Our findings suggested that whole-brain modeling of intrinsic dynamics may constitute a determinant for stratifying patients into treatment groups and bringing us closer towards precision medicine.

Proposal for an Integrative Cognitive-Emotional Conception of ADHD

Although numerous efforts have been made to deepen our understanding of the etiology of Attention Deficit Hyperactivity Disorder (ADHD), no explanation of its origins, nor of its consequences, has yet found a consensus within the scientific community. This study performs a theoretical review of various research studies and provides a reflection on the role of emotions in the origin of the disorder, at the neuroanatomical and functional level. To this end, theoretical models (single and multiple origin) and applied studies are reviewed in order to broaden the perspective on the relevance of the executive system in ADHD; it is suggested that this construct is not only composed and activated by cognitive processes and functions, but also includes elements of an emotional and motivational nature. Consequently, it is shown that ADHD is involved in social development and in a person's ability to adapt to the environment.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Reliability and subject specificity of personalized whole-brain dynamical models

Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.

Malingering and Stimulant Medications Abuse, Misuse and Diversion

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that interferes with multiple aspects of daily functioning. Malingering or feigning of symptoms can be a major challenge during ADHD assessment. Stimulant medication abuse, misuse and diversion may constitute another challenge during management. A literature search of the past 15 years on the topic continued to suggest that there are several reasons for malingering and faking ADHD symptoms. Some of the reasons include the intent to obtain prescriptions for stimulant medications for performance enhancement, to gain access to additional school services and accommodations, to use recreationally and to sell as a street drug. In some countries, patients may receive additional tax or student loan benefits. Several researchers suggested that self-report rating measures are easily simulated by patients without ADHD. They concluded that no questionnaire has proved sufficiently robust against false positives. Some clinical factors that may suggest malingering during the ADHD assessment are highlighted and some available tests to detect malingering are discussed.

Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study

Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor's Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.

Lifespan associations of resting-state brain functional networks with ADHD symptoms

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7–50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

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ADHD patients have a quadratic association with age in brain functional networks

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Limbic system better predicts hyperactivity across the lifespan

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Salient attention system better predicts the inattention across the lifespan

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The predictions on symptom scores are shared in ADHD children and adults

Towards an efficient validation of dynamical whole-brain models

Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)

Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. The sample was composed by 130 neurotypicals, 50 participants diagnosed with Schizophrenia, 49 with Bipolar disorder and 43 with ADHD. Single fMRI scans were reduced in their dimensionality by a novel method (i-ECO) averaging results per Region of Interest and through an additive color method (RGB): local connectivity values (Regional Homogeneity), network centrality measures (Eigenvector Centrality), spectral dimensions (fractional Amplitude of Low-Frequency Fluctuations). Average images per diagnostic group were plotted and described. The discriminative power of this novel method for visualizing and analyzing fMRI results in an integrative manner was explored through the usage of convolutional neural networks. The new methodology of i-ECO showed between-groups differences that could be easily appreciated by the human eye. The precision-recall Area Under the Curve (PR-AUC) of our models was > 84.5% for each diagnostic group as evaluated on the test-set – 80/20 split. In conclusion, this study provides evidence for an integrative and easy-to-understand approach in the analysis and visualization of fMRI results. A high discriminative power for psychiatric conditions was reached. This proof-of-work study may serve to investigate further developments over more extensive datasets covering a wider range of psychiatric diagnoses.