A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans.

Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation. Across a series of independent experiments, we demonstrate that αCLAS alters alpha power, frequency, and connectivity in a phase, amplitude, and topography-dependent manner. Using single-pulse-αCLAS, we show that the effects of auditory stimuli on alpha oscillations can be explained within the theoretical framework of oscillator theory and a phase-reset mechanism. Finally, we demonstrate the functional relevance of our approach by showing that αCLAS can interfere with sleep onset dynamics in a phase-dependent manner.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function.

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.

"Too much of a burden": Lived experiences of depressive rumination in early adulthood.

Rumination is an established transdiagnostic factor in mental illness, but there remains a significant gap in understanding the subjective experiences of those affected by it. This study explored the lived experiences of depressive rumination in early adulthood, a population notably susceptible to its effects. We interviewed 20 participants aged between 18 and 35 years using a semistructured approach and generated five distinct but interconnected themes using reflective thematic analysis. The first theme delved into recurrent narratives of past traumas and unresolved pain, with participants unable to move on from their past. The second theme illustrated how participants, due to real or perceived pressure, often equated their self-worth with their ability to meet expectations, leading to a distorted self-view and diminished self-esteem. The third theme captured the relentless pursuit of mental peace, with tranquility remaining ever elusive despite the frequent use of distraction. The fourth theme highlighted the profound isolation stemming from internalized mental health stigma, with participants grappling with fears of being perceived as burdensome and facing rejection from their close ones. Finally, the fifth theme underscored the far-reaching and interconnected repercussions of rumination on mental, emotional, and physical health and individuals' ability to achieve their life goals. These findings emphasize the intertwined nature of psychological, physiological, and social risk factors for the development and maintenance of rumination, advocating for a holistic treatment approach to rumination and paving the way for more timely, tailored care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Task-based differences in brain state dynamics and their relation to cognitive ability.

Transient patterns of interregional connectivity form and dissipate in response to varying cognitive demands. Yet, it is not clear how different cognitive demands influence brain state dynamics, and whether these dynamics relate to general cognitive ability. Here, using functional magnetic resonance imaging (fMRI) data, we characterised shared, recurrent, global brain states in 187 participants across the working memory, emotion, language, and relation tasks from the Human Connectome Project. Brain states were determined using Leading Eigenvector Dynamics Analysis (LEiDA). In addition to the LEiDA-based metrics of brain state lifetimes and probabilities, we also computed information-theoretic measures of Block Decomposition Method of complexity, Lempel-Ziv complexity and transition entropy. Information theoretic metrics are notable in their ability to compute relationships amongst sequences of states over time, compared to lifetime and probability, which capture the behaviour of each state in isolation. We then related task-based brain state metrics to fluid intelligence. We observed that brain states exhibited stable topology across a range of numbers of clusters (K = 2:15). Most metrics of brain state dynamics, including state lifetime, probability, and all information theoretic metrics, reliably differed between tasks. However, relationships between state dynamic metrics and cognitive abilities varied according to the task, the metric, and the value of K, indicating that there are contextual relationships between task-dependant state dynamics and trait cognitive ability. This study provides evidence that the brain reconfigures across time in response to cognitive demands, and that there are contextual, rather than generalisable, relationships amongst task, state dynamics, and cognitive ability.

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico.

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

Planning ahead: Predictable switching recruits task-active and resting-state networks.

Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.

Neural diffusivity and pre-emptive epileptic seizure intervention.

The propagation of epileptic seizure activity in the brain is a widespread pathophysiology that, in principle, should yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics. During a seizure, neural activity will deviate from its current dynamical regime to one in which there are significant signal fluctuations. In silico treatments of neural activity are an important tool for the understanding of how the healthy brain can maintain stability, as well as of how pathology can lead to seizures. The hope is that, contained within the mathematical foundations of such treatments, there lie potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one extends the framework to model the ways in which activity propagates along the structural connections of neural systems. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity-i.e., by lowering the rate at which neuronal fluctuations disperse to neighbouring regions. We show, for neural systems prone to epileptic seizures, that such a reduction in diffusivity can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror the activity profile of a pathological region in its functionally connected areas. This counter-intuitive method is intended to be used pre-emptively-i.e., in order to mitigate the effects of the seizure, or ideally even prevent it from occurring in the first place. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network prior to seizure onset. Our hope is that this technique can form the basis for future real-time monitoring and intervention devices that are capable of treating epilepsy in a non-invasive manner.

Dynamic causal modelling of phase-amplitude interactions.

Models of coupled phase oscillators are used to describe a wide variety of phenomena in neuroimaging. These models typically rest on the premise that oscillator dynamics do not evolve beyond their respective limit cycles, and hence that interactions can be described purely in terms of phase differences. Whilst mathematically convenient, the restrictive nature of phase-only models can limit their explanatory power. We therefore propose a generalisation of dynamic causal modelling that incorporates both phase and amplitude. This allows for the separate quantifications of phase and amplitude contributions to the connectivity between neural regions. We show, using model-generated data and simulations of coupled pendula, that phase-amplitude models can describe strongly coupled systems more effectively than their phase-only counterparts. We relate our findings to four metrics commonly used in neuroimaging: the Kuramoto order parameter, cross-correlation, phase-lag index, and spectral entropy. We find that, with the exception of spectral entropy, the phase-amplitude model is able to capture all metrics more effectively than the phase-only model. We then demonstrate, using local field potential recordings in rodents and functional magnetic resonance imaging in macaque monkeys, that amplitudes in oscillator models play an important role in describing neural dynamics in anaesthetised brain states.

A meta-analysis of functional magnetic resonance imaging studies of divergent thinking using activation likelihood estimation.

There are conflicting findings regarding brain regions and networks underpinning creativity, with divergent thinking tasks commonly used to study this. A handful of meta-analyses have attempted to synthesise findings on neural mechanisms of divergent thinking. With the rapid proliferation of research and recent developments in fMRI meta-analysis approaches, it is timely to reassess the regions activated during divergent thinking creativity tasks. Of particular interest is examining the evidence regarding large-scale brain networks proposed to be key in divergent thinking and extending this work to consider the role of the semantic control network. Studies utilising fMRI with healthy participants completing divergent thinking tasks were systematically identified, with 20 studies meeting the criteria. Activation Likelihood Estimation was then used to integrate the neuroimaging results across studies. This revealed four clusters: the left inferior parietal lobe; the left inferior frontal and precentral gyrus; the superior and medial frontal gyrus and the right cerebellum. These regions are key in the semantic network, important for flexible retrieval of stored knowledge, highlighting the role of this network in divergent thinking.

Traumatic axonal injury influences the cognitive effect of non-invasive brain stimulation.

Non-invasive brain stimulation has been widely investigated as a potential treatment for a range of neurological and psychiatric conditions, including brain injury. However, the behavioural effects of brain stimulation are variable, for reasons that are poorly understood. This is a particular challenge for traumatic brain injury, where patterns of damage and their clinical effects are heterogeneous. Here we test the hypothesis that the response to transcranial direct current stimulation following traumatic brain injury is dependent on white matter damage within the stimulated network. We used a novel simultaneous stimulation-MRI protocol applying anodal, cathodal and sham stimulation to 24 healthy control subjects and 35 patients with moderate/severe traumatic brain injury. Stimulation was applied to the right inferior frontal gyrus/anterior insula node of the salience network, which was targeted because our previous work had shown its importance to executive function. Stimulation was applied during performance of the Stop Signal Task, which assesses response inhibition, a key component of executive function. Structural MRI was used to assess the extent of brain injury, including diffusion MRI assessment of post-traumatic axonal injury. Functional MRI, which was simultaneously acquired to delivery of stimulation, assessed the effects of stimulation on cognitive network function. Anodal stimulation improved response inhibition in control participants, an effect that was not observed in the patient group. The extent of traumatic axonal injury within the salience network strongly influenced the behavioural response to stimulation. Increasing damage to the tract connecting the stimulated right inferior frontal gyrus/anterior insula to the rest of the salience network was associated with reduced beneficial effects of stimulation. In addition, anodal stimulation normalized default mode network activation in patients with poor response inhibition, suggesting that stimulation modulates communication between the networks involved in supporting cognitive control. These results demonstrate an important principle: that white matter structure of the connections within a stimulated brain network influences the behavioural response to stimulation. This suggests that a personalized approach to non-invasive brain stimulation is likely to be necessary, with structural integrity of the targeted brain networks an important criterion for patient selection and an individualized approach to the selection of stimulation parameters.

Probing cortical and sub-cortical contributions to instruction-based learning: Regional specialisation and global network dynamics.

Diverse cortical networks and striatal brain regions are implicated in instruction-based learning (IBL); however, their distinct contributions remain unclear. We use a modified fMRI paradigm to test two hypotheses regarding the brain mechanisms that underlie IBL. One hypothesis proposes that anterior caudate and frontoparietal regions transiently co-activate when new rules are being bound in working memory. The other proposes that they mediate the application of the rules at different stages of the consolidation process. In accordance with the former hypothesis, we report strong activation peaks within and increased connectivity between anterior caudate and frontoparietal regions when rule-instruction slides are presented. However, similar effects occur throughout a broader set of cortical and sub-cortical regions, indicating a metabolically costly reconfiguration of the global brain state. The distinct functional roles of cingulo-opercular, frontoparietal and default-mode networks are apparent from their activation throughout, early and late in the practice phase respectively. Furthermore, there is tentative evidence of a peak in anterior caudate activity mid-way through the practice stage. These results demonstrate how performance of the same simple task involves a steadily shifting balance of brain systems as learning progresses. They also highlight the importance of distinguishing between regional specialisation and global dynamics when studying the network mechanisms that underlie cognition and learning.

Cognitive enhancement with Salience Network electrical stimulation is influenced by network structural connectivity.

The Salience Network (SN) and its interactions are important for cognitive control. We have previously shown that structural damage to the SN is associated with abnormal functional connectivity between the SN and Default Mode Network (DMN), abnormal DMN deactivation, and impaired response inhibition, which is an important aspect of cognitive control. This suggests that stimulating the SN might enhance cognitive control. Here, we tested whether non-invasive transcranial direct current stimulation (TDCS) could be used to modulate activity within the SN and enhance cognitive control. TDCS was applied to the right inferior frontal gyrus/anterior insula cortex during performance of the Stop Signal Task (SST) and concurrent functional (f)MRI. Anodal TDCS improved response inhibition. Furthermore, stratification of participants based on SN structural connectivity showed that it was an important influence on both behavioural and physiological responses to anodal TDCS. Participants with high fractional anisotropy within the SN showed improved SST performance and increased activation of the SN with anodal TDCS, whilst those with low fractional anisotropy within the SN did not. Cathodal stimulation of the SN produced activation of the right caudate, an effect which was not modulated by SN structural connectivity. Our results show that stimulation targeted to the SN can improve response inhibition, supporting the causal influence of this network on cognitive control and confirming it as a target to produce cognitive enhancement. Our results also highlight the importance of structural connectivity as a modulator of network to TDCS, which should guide the design and interpretation of future stimulation studies.

Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation.

Despite its widespread use in cognitive studies, there is still limited understanding of whether and how transcranial direct current stimulation (tDCS) modulates brain network function. To clarify its physiological effects, we assessed brain network function using functional magnetic resonance imaging (fMRI) simultaneously acquired during tDCS stimulation. Cognitive state was manipulated by having subjects perform a Choice Reaction Task or being at "rest." A novel factorial design was used to assess the effects of brain state and polarity. Anodal and cathodal tDCS were applied to the right inferior frontal gyrus (rIFG), a region involved in controlling activity large-scale intrinsic connectivity networks during switches of cognitive state. tDCS produced widespread modulation of brain activity in a polarity and brain state dependent manner. In the absence of task, the main effect of tDCS was to accentuate default mode network (DMN) activation and salience network (SN) deactivation. In contrast, during task performance, tDCS increased SN activation. In the absence of task, the main effect of anodal tDCS was more pronounced, whereas cathodal tDCS had a greater effect during task performance. Cathodal tDCS also accentuated the within-DMN connectivity associated with task performance. There were minimal main effects of stimulation on network connectivity. These results demonstrate that rIFG tDCS can modulate the activity and functional connectivity of large-scale brain networks involved in cognitive function, in a brain state and polarity dependent manner. This study provides an important insight into mechanisms by which tDCS may modulate cognitive function, and also has implications for the design of future stimulation studies.

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning.

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword-object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary.SIGNIFICANCE STATEMENT Learning a task involves the brain system within which that specific task becomes established. Therefore, successfully learning a new vocabulary establishes the novel words in the language system. However, there is evidence that in the early stages of learning, networks within multiple-demand cortex (MDC), which control higher cognitive functions, such as working memory, attention, and monitoring of performance, become active. This activity declines once the task is learnt. The present study demonstrated that a node within MDC, located in midline frontal cortex, becomes active during the early stage of learning a novel vocabulary. Importantly, noninvasive brain stimulation of this node improved performance during this stage of learning. This observation demonstrated that MDC activity is important for learning.

Mitochondrial pathophysiology beyond the retinal ganglion cell: occipital GABA is decreased in autosomal dominant optic neuropathy.

PURPOSE: It has remained a mystery why some genetic mitochondrial disorders affect predominantly specific cell types such as the retinal ganglion cell. This is particularly intriguing concerning retinal and cortical function since they are tightly linked in health and disease. Autosomal dominant optic neuropathy (ADOA) is a mitochondrial disease that affects the ganglion cell. However, it is unknown whether alterations are also present in the visual cortex, namely in excitation/inhibition balance. METHODS: In this study, we performed in vivo structural and biochemical proton magnetic resonance imaging in 14 ADOA and 11 age-matched control participants focusing on the visual cortex, with the aim of establishing whether in this genetically determined disease an independent cortical neurochemical phenotype could be established irrespective of a putative structural phenotype. Cortical thickness of anatomically defined visual areas was estimated, and a voxel-based morphometry approach was used to assess occipital volumetric changes in ADOA. Neurochemical measurements were focused on γ-aminobutyric acid (GABA) and glutamate, as indicators of the local excitatory/inhibitory balance. RESULTS: We found evidence for reduced visual cortical GABA and preserved glutamate concentrations in the absence of cortical or subcortical atrophy. These changes in GABA levels were explained by neither structural nor functional measures of visual loss, suggesting a developmental origin. CONCLUSIONS: These results suggest that mitochondrial disorders that were previously believed to only affect retinal function may also affect cortical physiology, especially the GABAergic system, suggesting reduced brain inhibition vs. excitation. This GABA phenotype, independent of sensory loss or cortical atrophy and in the presence of preserved glutamate levels, suggests a neurochemical developmental change at the cortical level, leading to a pathophysiological excitation/inhibition imbalance.

Translocator positron-emission tomography and magnetic resonance spectroscopic imaging of brain glial cell activation in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is characterised by a diffuse inflammatory response mediated by microglia and astrocytes. Brain translocator protein (TSPO) positron-emission tomography (PET) and [myo-inositol] magnetic resonance spectroscopy (MRS) were used together to assess this. OBJECTIVE: To explore the in vivo relationships between MRS and PET [11C]PBR28 in MS over a range of brain inflammatory burden. METHODS: A total of 23 patients were studied. TSPO PET imaging with [11C]PBR28, single voxel MRS and conventional magnetic resonance imaging (MRI) sequences were undertaken. Disability was assessed by Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite (MSFC). RESULTS: [11C]PBR28 uptake and [ myo-inositol] were not associated. When the whole cohort was stratified by higher [11C]PBR28 inflammatory burden, [ myo-inositol] was positively correlated to [11C]PBR28 uptake (Spearman's ρ = 0.685, p = 0.014). Moderate correlations were found between [11C]PBR28 uptake and both MRS creatine normalised N-acetyl aspartate (NAA) concentration and grey matter volume. MSFC was correlated with grey matter volume (ρ = 0.535, p = 0.009). There were no associations between other imaging or clinical measures. CONCLUSION: MRS [ myo-inositol] and PET [11C]PBR28 measure independent inflammatory processes which may be more commonly found together with more severe inflammatory disease. Microglial activation measured by [11C]PBR28 uptake was associated with loss of neuronal integrity and grey matter atrophy.

The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1.

GABA deficit in the visual cortex of patients with neurofibromatosis type 1: genotype-phenotype correlations and functional impact.

Alterations in the balance between excitatory and inhibitory neurotransmission have been implicated in several neurodevelopmental disorders. Neurofibromatosis type 1 is one of the most common monogenic disorders causing cognitive deficits for which studies on a mouse model (Nfl(+/-)) proposed increased γ-aminobutyric acid-mediated inhibitory neurotransmission as the neural mechanism underlying these deficits. To test whether a similar mechanism translates to the human disorder, we used magnetic resonance spectroscopy to measure γ-aminobutyric acid levels in the visual cortex of children and adolescents with neurofibromatosis type 1 (n = 20) and matched control subjects (n = 26). We found that patients with neurofibromatosis type 1 have significantly lower γ-aminobutyric acid levels than control subjects, and that neurofibromatosis type 1 mutation type significantly predicted cortical γ-aminobutyric acid. Moreover, functional imaging of the visual cortex indicated that blood oxygen level-dependent signal was correlated with γ-aminobutyric acid levels both in patients and control subjects. Our results provide in vivo evidence of γ-aminobutyric acidergic dysfunction in neurofibromatosis type 1 by showing a reduction in γ-aminobutyric acid levels in human patients. This finding is relevant to understand the physiological profile of the disorder and has implications for the identification of targets for therapeutic strategies.

Audio-visual multisensory integration and haptic perception are altered in adults with developmental coordination disorder.

Developmental Coordination Disorder (DCD) is a movement disorder in which atypical sensory processing may underly movement atypicality. However, whether altered sensory processing is domain-specific or global in nature, are unanswered questions. Here, we measured for the first time, different aspects of sensory processing and spatiotemporal integration in the same cohort of adult participants with DCD (N = 16), possible DCD (pDCD, N = 12) and neurotypical adults (NT, N = 28). Haptic perception was reduced in both DCD and the extended DCD + pDCD groups when compared to NT adults. Audio-visual integration, measured using the sound-induced double flash illusion, was reduced only in DCD participants, and not the DCD + pDCD extended group. While low-level sensory processing was altered in DCD, the more cognitive, higher-level ability to infer temporal dimensions from spatial information, and vice-versa, as assessed with Tau-Kappa effects, was intact in DCD (and extended DCD + pDCD) participants. Both audio-visual integration and haptic perception difficulties correlated with the degree of self-reported DCD symptoms and were most apparent when comparing DCD and NT groups directly, instead of the expanded DCD + pDCD group. The association of sensory difficulties with DCD symptoms suggests that perceptual differences play a role in motor difficulties in DCD via an underlying internal modelling mechanism.