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Item does not contain fulltext In this chapter, we provide an overview of different types of cognitive enhancement techniques that target key neural regions related to dyslexia and dyscalculia. The main emphasis is on non-invasive brain stimulation (NIBS), such as transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). The former sends a pulse of current through a coil to evoke a magnetic field that penetrates the skull and enters the brain. The direction of neuromodulation by TMS is controlled by the stimulation frequency.

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

Non-invasive brain stimulation (NIBS) has been highlighted as a powerful tool to promote neuroplasticity, and an attractive approach to support cognitive remediation. Here we provide a systematic review of 26 papers using NIBS to ameliorate cognitive dysfunctions in three prevalent neurodevelopmental disorders: Attention-Deficit/Hyperactivity Disorder (ADHD), Developmental Dyslexia and Developmental Dyscalculia. An overview of the state of research shows a predominance of studies using repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) techniques, and an unequal distribution among clinical conditions. Regarding the utility of NIBS, the results are promising but also ambiguous. Twenty-three papers reported beneficial effects, but many of these effects were found only once or were only partially replicated and some studies even reported detrimental effects. Furthermore, most studies differed in at least one core aspect, the NIBS applied, the questionnaires and cognitive tests conducted, or the age group investigated, and sample sizes were mostly small. Hence, further studies are needed to rigorously examine the potential of NIBS in the remediation of cognitive functions. Finally, we discuss potential caveats and future directions. We reason that if adequately addressing these challenges NIBS can be feasible, with potential benefits in treating neurodevelopmental disorders.

Item does not contain fulltext Numerical skills encompass a variety of cognitive processes and are crucial for performance in today's modern world but vary greatly between individuals. Several approaches of numerical cognition have been studied ranging from behavioral to neuroimaging studies. Transcranial electrical stimulation (tES) has become a promising tool to influence numerical cognition and is frequently used in clinical settings. The main aim of this chapter is to shed light onto current tES research as an intervention in this domain. We first provide a brief overview of the underlying neurocognitive mechanisms of basic and advanced mathematical skills, such as working memory, executive functions, and (non)symbolic number skills, since tES allows to intervene and further unravel the role of these mechanisms. In addition, we discuss the need for tES research to focus on the transfer of these skills to a similar numerical task to determine facilitation by means of neuroplasticity. Therefore we emphasize studies using tES as a numerical intervention and focus on transfer effects since these outcomes could contribute to implications for educational practice.

Contains fulltext : 208139.pdf (Publisher’s version ) (Open Access) Studies in several domains of expertise have established that experience-dependent plasticity brings about both functional and anatomical changes. However, little is known about how such changes come to shape the brain in the case of expertise acquired by professional mathematicians. Here, we aimed to identify cognitive and brain-structural (grey and white matter) characteristics of mathematicians as compared to non-mathematicians. Mathematicians and non-mathematician academics from the University of Oxford underwent structural and diffusion MRI scans, and were tested on a cognitive battery assessing working memory, attention, IQ, numerical and social skills. At the behavioural level, mathematical expertise was associated with better performance in domain-general and domain-specific dimensions. At the grey matter level, in a whole-brain analysis, behavioural performance correlated with grey matter density in left superior frontal gyrus - positively for mathematicians but negatively for non-mathematicians; in a region of interest analysis, we found in mathematicians higher grey matter density in the right superior parietal lobule, but lower grey matter density in the right intraparietal sulcus and in the left inferior frontal gyrus. In terms of white matter, there were no significant group differences in fractional anisotropy or mean diffusivity. These results reveal new insights into the relationship between mathematical expertise and grey matter metrics in brain regions previously implicated in numerical cognition, as well as in regions that have so far received less attention in this field. Further studies, based on longitudinal designs and cognitive training, could examine the conjecture that such cross-sectional findings arise from a bidirectional link between experience and structural brain changes that is itself subject to change across the lifespan. 11 p.

Various methods have been attempted to effectively ameliorate psychiatric and neurological conditions in children and adults. One of the attractive ideas is to develop interventions to create a lasting, rather than only an immediate, effect. Neurostimulation has been shown to yield long-term effect when combined with cognitive training in healthy young adults. We examined whether such approach could benefit children with attention deficit hyperactivity disorder (ADHD), the most common neurodevelopmental disorder in childhood. We used a randomized double-blind active-controlled crossover study of 19 unmedicated children (aged 7–12 years old) with attention deficit hyperactivity disorder, who received either transcranial direct current stimulation or random noise stimulation while completing five-day executive functions training, which includes working memory, cognitive flexibility, and inhibition tasks. Both stimulation protocols have previously shown potential for inducing lasting benefits in adults, while transcranial direct current stimulation was examined in multiple attention deficit hyperactivity disorder studies and has been highlighted as a promising method for treating neuropsychological deficits. For our primary outcome, transcranial random noise stimulation yielded a clinical improvement as indicated by the reduced attention deficit hyperactivity disorder rating scale score from baseline, and in comparison to the changes observed in transcranial direct current stimulation. Moreover, the effect of brain stimulation one week after completion of treatment yielded further improvement, suggesting a neuroplasticity-related effect. Finally, transcranial random noise stimulation improved working memory compared to transcranial direct current stimulation, and a larger transcranial random noise stimulation effect on attention deficit hyperactivity disorder rating scale was predicted for those patients who showed the greatest improvement in working memory. Our results provide a promising direction toward a novel intervention in attention deficit hyperactivity disorder, which is shown to have a lasting effect via the modulating of neuroplasticity, rather than a merely immediate effect as was shown for in previous medical interventions.

Numerical cognition is critical for modern life; however, the precise neural mechanisms underpinning numerical magnitude allocation in humans remain obscure. Based upon previous reports demonstrating the close behavioral and neuro-anatomical relationship between number allocation and spatial attention, we hypothesized that these systems would be subject to similar control mechanisms, namely dynamic interhemispheric competition. We employed a physiological paradigm, combining visual and vestibular stimulation, to induce interhemispheric conflict and subsequent unihemispheric inhibition, as confirmed by transcranial direct current stimulation (tDCS). This allowed us to demonstrate the first systematic bidirectional modulation of numerical magnitude toward either higher or lower numbers, independently of either eye movements or spatial attention mediated biases. We incorporated both our findings and those from the most widely accepted theoretical framework for numerical cognition to present a novel unifying computational model that describes how numerical magnitude allocation is subject to dynamic interhemispheric competition. That is, numerical allocation is continually updated in a contextual manner based upon relative magnitude, with the right hemisphere responsible for smaller magnitudes and the left hemisphere for larger magnitudes.

This article presents a model for regulating cognitive enhancement devices (CEDs). Recently, it has become very easy for individuals to purchase devices which directly modulate brain function. For example, transcranial direct current stimulators are increasingly being produced and marketed online as devices for cognitive enhancement. Despite posing risks in a similar way to medical devices, devices that do not make any therapeutic claims do not have to meet anything more than basic product safety standards. We present the case for extending existing medical device legislation to cover CEDs. Medical devices and CEDs operate by the same or similar mechanisms and pose the same or similar risks. This fact coupled with the arbitrariness of the line between treatment and enhancement count in favour of regulating these devices in the same way. In arguing for this regulatory model, the paper highlights potential challenges to its implementation, and suggests solutions.

Cognitive training offers the potential for individualised learning, prevention of cognitive decline, and rehabilitation. However, key research challenges include ecological validity (training design and duration), transfer of learning and long-term effects. Given that cognitive training and neuromodulation affect neuroplasticity, their combination could promote greater, synergistic effects. We investigated whether combining transcranial direct current stimulation (tDCS) with cognitive training could further enhance cognitive performance compared to training alone, and promote transfer within a short period of time. Healthy adults received real or sham tDCS over their dorsolateral prefrontal cortices during two 30-minute mathematics training sessions involving body movements. To examine the role of training, an active control group received tDCS during a non-mathematical task. Those who received real tDCS performed significantly better in the game than the sham group, and showed transfer effects to working memory, a related but non-numerical cognitive domain. This transfer effect was absent in active and sham control groups. Furthermore, training gains were more pronounced amongst those with lower baseline cognitive abilities, suggesting the potential for reducing cognitive inequalities. All effects associated with real tDCS remained 2 months post-training. Our study demonstrates the potential benefit of this approach for long-term enhancement of human learning and cognition

Developmental adjustments in the balance of excitation and inhibition are thought to constrain the plasticity of sensory areas of the cortex. It is unknown however, how changes in excitatory or inhibitory neurochemical expression (glutamate, g-aminobutyric acid (GABA)) contribute to skill acquisition during development. Here we used single-voxel proton magnetic resonance spectroscopy (1 H-MRS) to reveal how differences in cortical glutamate vs. GABA ratios relate to face proficiency and working memory abilities in children and adults. We show that higher glutamate levels in the inferior frontal gyrus correlated positively with face processing proficiency in the children, but not the adults, an effect which was independent of age-dependent differences in underlying cortical gray matter. Moreover, we found that glutamate/GABA levels and gray matter volume are dissociated at the different maturational stages. These findings suggest that increased excitation during development is linked to neuroplasticity and the acquisition of new cognitive skills. They also offer a new, neurochemical approach to investigating the relationship between cognitive performance and brain development across the lifespan.

Mathematical abilities that are correlated with various life outcomes vary across individuals. One approach to improve mathematical abilities is by understanding the underlying cognitive functions. Theoretical and experimental evidence suggest that mathematical abilities are subserved by "core" and "noncore" skills. Core skills are commonly regarded as the "innate" capacity to attend to and process numerical information, while noncore skills are those that are important for mathematical cognition, but are not exclusive to the mathematical domain such as executive functions, spatial skills, and attention. In recent years, mathematical training has been combined with the application of noninvasive brain stimulation to further enhance training outcomes. However, the development of more strategic training paradigms is hindered by the lack of understanding on the contributory nature of core and noncore skills and their neural underpinnings. In the current review, we will examine the effects of brain stimulation with focus on transcranial electrical stimulation on core and noncore skills, and its impact on mathematical and numerical training. We will conclude with a discussion on the theoretical and experimental implications of these studies and directions for further research.

In a thought-provoking paper, Simner (2012) highlights and criticizes a number of assumptions concerning synaesthesia. She specifically takes issue with the following assumptions: (1) synaesthesia is strictly a sensory-perceptual phenomenon; (2) consistency of inducer-concurrent pairs is the gold standard for establishing the authenticity of an individual's synaesthesia; and (3) synaesthesia is not heterogeneous. In the wake of this critique, Simner advances a working definition of synaesthesia as a neurological hyper-association that aims to be more inclusive of its variants. We are very sympathetic to Simner's approach and believe that it raises important points that will advance our understanding of synaesthesia. Here we supplement, and sometimes challenge, some of these ideas.

Many studies have suggested that the intraparietal sulcus (IPS), particularly in the dominant hemisphere, is crucially involved in numerical comparisons. However, this parietal structure has been found to be involved in other tasks that require spatial processing or visuospatial attention as well. fMRI was used to investigate three different magnitude comparisons in an event-related-block design: (a) Which digit is larger in numerical value (e.g., 2 or 5)? (b) Which digit is brighter (e.g., 3 or 3)? (c) Which digit is physically larger (e.g., 3 or 3)? Results indicate a widespread cortical network including a bilateral activation of the intraparietal sulci for all different comparisons. However, by computing contrasts of brain activation between the respective comparison conditions and applying a cortical distance effect as an additional criterion, number-specific activation was revealed in left IPS and right temporal regions. These results indicate that there are both commonalities and differences in the spatial layout of the brain systems for numerical and physical comparisons and that especially the left IPS, while involved in magnitude comparison in general, plays a special role in number comparison.

Modulating the temporoparietal junction (TPJ), especially the right counterpart, shows promises in enhancing social cognitive ability. However, it is ambiguous whether the functional lateralization of TPJ determines people’s responsiveness to brain stimulation. Here we investigate this issue with an individual difference approach. Forty-five participants attended three sessions of transcranial direct current stimulation (tDCS) experiments and one neuroimaging session. Our results support the symmetric mechanism of left and right TPJ stimulation. Firstly, the left and right TPJ stimulation effect were comparable in the group-level analysis. Secondly, the individual-level analysis revealed that a less right-lateralized TPJ was associated with a higher level of responsiveness. Participants could be classified into positive responders showing cognitive enhancement and negative responders showing cognitive impairment due to stimulation. The positive responders showed weaker connectivity between bilateral TPJ and the medial prefrontal cortex, which mediated the prediction of offline responsiveness by the lateralization and the social-related trait. Our findings call for a better characterization and predictive models for whom tDCS should be used for, and highlight the necessity and feasibility of pre-stimulation screening.

Contains fulltext : 292961.pdf (Publisher’s version ) (Open Access) This PhD thesis focuses on using brain stimulation to increase people's arithmetic skills and to examine the underlying neural processes. Transcranial electrical stimulation (tES) is a technique capable of mapping the relationship between behaviour and brain function. A major limitation of brain stimulation is that it does not take into account differences between people. In the first two chapters, it becomes clear that individual differences are not accounted for in studies using brain stimulation as a numerical intervention. The next chapter examines several studies that aim to improve learning in dyscalculia and dyslexia. Two other studies show that there are both important behavioural markers (baseline numeracy) and neural markers (excitation/inhibition levels) for the efficacy of tES. One of these studies also shows the importance of personalising brain stimulation, thereby emphasising individual differences using machine learning. The final chapter shows that the way researchers analyse electrophysiological (EEG) data matters and is important when drawing conclusions in relation to behaviour. In short, brain stimulation is an effective method to influence numeracy and learning processes. Individual differences are also important. Radboud University, 15 juni 2023 Promotores : Kroesbergen, E.H., Cohen Kadosh, R., Roelofs, K. Co-promotor : Ven, S.H.G. van der 231 p.

Several psychophysical and neuropsychological investigations suggest that the processing of number and spatial information is strongly associated. A popular account for this bulk of evidence argues that this association has its origin in the underlying mental representation of numbers taking the form of a horizontally oriented mental number line, which is isomorphic to the representation of physical lines. Recently however, several alternative explanations have been put forward. We describe those theories and argue that no single current framework is on itself able to explain the full range of observations. To do this, a hybrid account is proposed which takes into account the underlying representation but emphasizes the processing mechanisms required by the task at hand.

An increasingly utilised tool in cognitive neuroscience, which is capable of drawing causal relationships between brain function and behaviour, is transcranial electrical stimulation (tES). However, this non-invasive brain stimulation technique is commonly shown to have high levels of variability when used to modulate cognitive abilities. A potential cause of this variability is the requirement for researchers to select a number of stimulation parameters such as current intensity and frequency prior to delivering stimulation to an individual. As there is a very large number of possible parameter combinations, the selection of an optimal combination with which to modulate the behaviour of interest is highly challenging. In this thesis I employ a machine learning technique called Bayesian Optimisation (BO) to automatically search through the large stimulation parameter space to identify the optimal parameters for improving cognitive performance in complex behavioural tasks. Initially, I utilise a 'standard' BO algorithm to identify optimal parameters for improving performance in a behavioural task investigating rapid instructed task learning (Chapter 3). In a subsequent chapter I validate the results of this BO algorithm in a separate study (Chapter 4). I then attempt to extend the BO algorithm to enable it to suggest personalised stimulation parameters based on an individual's personal variables. I first apply this to a sustained attention task whilst utilising a personalised variable of individual theta:beta ratio (Chapter 5). Subsequently, I apply the personalised BO (pBO) to an arithmetic task whilst considering an individual's baseline ability as the personalised variable (Chapter 6). Finally, I validate the results of the pBO algorithm in a separate study (Chapter 7). Overall, this thesis explores the use of BO algorithms to suggest parameters for neurointerventions in complex cognitive behaviours, both at a 'one-size-fits-all' and a personalised level. Whilst results from initial optimisation studies are promising, conflicting results in validation studies highlight the difficulties of applying BO to complex human behaviour and the need for validation in further work.

Humans have an innate ability to deal with numerosity and other aspects of magnitude. This ability is generally honed through education in and experience with mathematics, which necessarily changes the brain structurally and functionally. These changes can be further manipulated through non-invasive electrical brain stimulation. Studying these processes in the case of maths not only constitutes research of great practical impact – given the importance of numerical skills in today's society – but also makes use of maths as a suitable domain in which to study plasticity. In this thesis, I aimed to explore how expertise with numbers shapes brain and behaviour, and also the degree to which processing numbers is similar to other domains in terms of the necessity of healthy brain regions believed to underlie normal processing within and across these domains. In Study 1, behavioural and structural brain differences were found cross-sectionally between mathematicians and non-mathematicians. A double dissociation between those groups was found between grey matter density in the frontal lobe and behavioural performance: their correlation was positive for mathematicians but negative for controls. These effects may have been caused by years of experience, by congenital predispositions, or, plausibly, by both of these factors, whose disambiguation is non-trivial. Study 2 used transcranial random noise stimulation (tRNS) to assist arithmetic learning. A novel montage was used to enhance brain function during the stage when it is believed to be most involved. Real as compared to sham tRNS enhanced reaction times (RTs) and learning rate on a calculation-based task, but not on a retrieval-based task. The effects were only observed in conditions of high task difficulty. Study 3 examined structural MRI measures before and after arithmetic training to determine how either frontal or parietal tRNS applied with the task changes the structure of the brain longitudinally as compared to sham. Previous results (including those of Study 2) of behavioural facilitation in terms of enhanced RTs to calculation problems were replicated, and further interpreted. Both frontal and parietal tRNS modulated the changes that occurred, pre-to-post training, in terms of cortical volume and gyrification of frontal, parietal and temporal areas. Study 4 investigated the shared neural and cognitive resources used for processing numerical magnitude and musical pitch, by probing how stimulus-response compatibility (SRC) effects for each of the two dimensions compare in a group of mainly temporoparietal lesion patients with numerical impairments versus controls. A double dissociation was found in that numerically impaired patients did not show the number-based SRC effect but did show the pitch-based one, while control subjects demonstrated the opposite trend. Overall, the results of these studies leave us with three main messages. First, expertise in numbers and mathematics, whether acquired through years of experience (Study 1) or through a few days of tRNS-assisted training (Study 3), appears to be associated with complex changes in the morphology of several brain structures. Some – but not all – of these structures are maths-relevant, and, in the case of tRNS-assisted training, they are distal to the site of the stimulating electrodes. Second, tRNS can improve performance in arithmetic (Studies 2 and 3), although the mechanisms by which this occurs are not yet fully understood, neither neurally nor behaviourally. Third, I found (Study 4) that brain lesions leading to impairment in the number domain do not necessarily affect processing in other domains – such as pitch – that are otherwise linked to number via a putative common code in the parietal lobes.