Dynamic Configuration of Large-Scale Cortical Networks: A Useful Framework for Clarifying the Heterogeneity Found in Attention-Deficit/Hyperactivity Disorder

The heterogeneity of attention-deficit/hyperactivity disorder(ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500–0ms, 0–500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity

Neuroplasticity, neural reuse, and the language module

What conception of mental architecture can survive the evidence of neuroplasticity and neural reuse in the human brain? In particular, what sorts of modules are compatible with this evidence? I aim to show how developmental and adult neuroplasticity, as well as evidence of pervasive neural reuse, forces us to revise the standard conception of modularity and spells the end of a hardwired and dedicated language module. I argue from principles of both neural reuse and neural redundancy that language is facilitated by a composite of modules (or module-like entities), few if any of which are likely to be linguistically special, and that neuroplasticity provides evidence that (in key respects and to an appreciable extent) few if any of them ought to be considered developmentally robust, though their development does seem to be constrained by features intrinsic to particular regions of cortex (manifesting as domain-specific predispositions or acquisition biases). In the course of doing so I articulate a schematically and neurobiologically precise framework for understanding modules and their supramodular interactions

Neoliterate adult dyslexia and literacy policies : a neurocognitive research review of a curious unexplored phenomenon

There are about 750 million adult illiterates who in principle could learn fluent reading. However, adult literacy programs have performed poorly. Various social and operational reasons may be responsible. This paper explores the role of some neurocognitive reasons in adult performance. Automatic readers of a script detect letters and words effortlessly and involuntarily. Adults learning new scripts find it hard to attain this performance. Whether illiterate or educated, adults learning a new script detect letters slowly, may make mistakes, understand little, soon abandon the task, and may also forget what they learned. When neoliterates glance at a text, they often see a jumble of letters and may process only a few of their features. They must activate reading consciously andsound out each letter. The difficulties are perceptual, and interviews suggest that perceptual distortions may continue for decades. This phenomenon called “neoliterateadult dyslexia” (NAD) has escaped attention, possibly because few educated adults need to learn new scripts, and because the adult literacy failures are often attributed to social reasons. The phenomenon also may have been missed because researchers of perceptual learning use simpler stimuli. Automaticity in reading musical notation and air traffic control may reflect similar age-related learning difficulties. In the brain, the problem may originate at the early stages of the parietal cortex at the dorsal reading path, which constricts short-term visual memory. The visual areas V1 and perhaps V4 may also be involved. Deficits affect the ventral path that provides parallel processing and direct ‘print-to-meaning’ reading. Some neuronal groups may have a sensitive period that affects the capacity to collect frequency data and to integrate the appropriate features of letters and words. Then adults do not learn to perceive letter shapes and words as easily as most children do. A lack of data and research makes it difficult to design effective interventions.The adults’ difficulties are not linguistic. Dysfluent readers simply cannot decipher the symbols in sufficient time to get to the meaning of texts, or they do so after considerable conscious visual effort. Therefore language competence seems to have little relationship to the visuospatial tasks described in this document. Language knowledge does help predict likely words when judgements must be made on the basis of just a few letter features, but the relative ease of linguistic identification may lead to reading errors. The readers’ symptoms resonate with descriptions of severe and unremitting developmental dyslexia. Certain perceptual deficits may arise during adolescence and become more severe in adulthood. Some adults may become better readers than others. But learning a script at increasingly later ages seems related to worse outcomes, though no data exist to map this trajectory. To explore this curious phenomenon, this review brings together a range of insights from of neurocognitive research, notably studies on (a) perceptual learning, including studies on feature integration and face recognition; (b) neurocognitive studies aimed at dyslexic children, (c) studies of adults suffering from brain damage that causes alexia, and (d) performance of adult literacy programs. Implications and potential remedies are also presented. The author posits the hypothesis that perhaps all people become dyslexics for new alphabets at about age 19, and thatability to read new alphabets fluently decreases with age. Neoliterate adult dyslexia (NAD) may partly account for the difficulties of adult literacy programs. Thus it seems to impact about 750 million adult illiterates. For this reason, the paper calls for urgent research into this phenomenon

A systematic study of personification in synaesthesia: Behavioural and neuroimaging studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn synaesthetic personification, personality traits and other human characteristics are attributed to linguistic sequences and objects. Such non-perceptual concurrents are different from those found in most frequently studied types of synaesthesia, in which the eliciting stimuli induce sensory experiences. Here, subjective reports from synaesthetes were analysed and the cognitive and neural mechanisms underlying personification were investigated. Specifically, the neural bases of personification were examined using functional MRI in order to establish whether brain regions implicated in social cognition are involved in implementing personification. Additional behavioural tests were used to determine whether personification of inanimate objects is automatic in synaesthesia. Subjective reports describing general characteristics of synaesthetic personification were collected using a semi-structured questionnaire. A Stroop-like paradigm was developed in order to examine the automaticity of object personification, similarly to the previous investigations. Synaesthetes were significantly slower in responding to incongruent than to congruent stimuli. This difference was not found in the control group. The functional neuroimaging investigations demonstrated that brain regions involved in synaesthetic personification of graphemes and objects partially overlap with brain areas activated in normal social cognition, including the temporo-parietal junction, precuneus and posterior cingulate cortex. Activations were observed in areas known to be correlated with mentalising, reflecting the social and affective character of concurrents described in subjective reports. Psychological factors linked with personification in previous studies were also assessed in personifiers, using empathy, mentalising and loneliness scales. Neither heightened empathy nor mentalising were found to be necessary for personification, but personifying synaesthetes in the study felt lonelier than the general population, and this was more pronounced in those who personified more. These results demonstrate that personification shares many defining characteristics with classical forms of synaesthesia. Ascribing humanlike characteristics to graphemes and objects is a spontaneous and automatic process, inducer-concurrent pairings are consistent over time and the phenomenological character of concurrents is reflected in functional neuroanatomy. Furthermore, the neuroimaging findings are consistent with the suggestions that synaesthetes have a lower threshold for activation brain regions implicated in self-projection and mentalising, which may facilitate the personification processes in synaesthesia.Brunel University - Isambard Scholarshi

Individual Differences in Memory Functions and Their Relation to Hippocampal Connectivity

The hippocampus plays an important role in many aspects of learning and memory. It is most known for its role in episodic memory and spatial navigation, though it has also been shown to contribute to other processes like prioritizing memory for motivationally salient information and connecting related memories to form generalized knowledge. How can a single structure support different types of learning? As the hippocampus does not work in isolation to support memory, one proposal is that it may form connections with different brain regions to support different functions of memory. Recent work has shown how stable, trait-like connections can be leveraged to predict individual behavior. Thus, the present dissertation aims to explore 1) how different hippocampal connections relate to different memory processes, and 2) whether intrinsic hippocampal connections can be linked to individual memory performance. In three empirical chapters, I demonstrate how distinct hippocampal connections are associated with different functions of memory, including reward motivated learning, generalization and memory specificity. Moreover, I show how anterior and posterior hippocampus form distinct connections that may further support different aspects of memory. Finally, the dissertation demonstrates how stable, trait-like hippocampal connections can be linked to individual behavior. Together, these findings provide insight into the different functions of hippocampal connectivity and the utility of intrinsic connections in understanding individual memory abilities

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Leverhulme Trust (RF-2011-811 378), the Alan Turing Institute (TU/B/000095), the Wellcome Trust (205067/Z/16/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, AEW from the Wellcome Trust (095183/Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746, PT from Engineering and Physical Sciences Research Council (EP/L000296/1), PEV from the MRC (MR/K020706/1)

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

The Biological Basis of Rapid Instructed Task Learning

The uniquely human ability to rapidly learn novel tasks from instructions is extremely important in everyday life, and yet its evolutionary origin and basis in the brain remain mysteries. In order to address these gaps in scientific knowledge, comparative human-monkey studies were consulted to predict the human brain areas involved in rapid instructed task learning (RITL). These predictions were tested using functional MRI (fMRI), magnetoencephalography (MEG), and a novel cognitive paradigm developed to systematically investigate the neural basis of RITL for the first time. In accordance with cross-species neuroanatomical differences, anterior prefrontal cortex (aPFC), anterior temporal lobe (aTL), dorsolateral prefrontal cortex (DLPFC), and posterior parietal cortex (PPC) were found to be involved in RITL. DLPFC and PPC formed a network involved in loading individual task semantics into working memory, while aPFC and aTL formed a network involved in integrating semantics in preparation for task performance. Both networks supported novel task set formation, which occurred in a bottom-up manner (semantic loading, then integration), and practiced task set retrieval, which occurred in a top-down manner (integration retrieval, then semantic loading). These findings suggest that RITL relies upon semantic loading by DLPFC and PPC, but that aPFC and aTL support semantic integration both dynamically during RITL and from long-term memory after extensive practice. More broadly, the findings suggest RITL is enabled in humans via a combination of enhanced symbolic processing (language), enhanced working memory manipulation (aPFC), and enhanced integrated semantic representation (aTL). The present document begins with a broad overview of RITL and related topics, such as its relation to animal cognition, other forms of learning, and cognitive control. These topics support several novel hypotheses regarding RITL and its likely basis in the brain. The fMRI study is then presented, verifying many of the hypotheses developed in the previous section. The MEG study is reported next, clarifying many of the questions about timing and causality suggested by the fMRI results. Finally, a general discussion integrates the results from both studies, expanding conclusions with an overview of brain connectivity findings, cross-species differences, and the role of neural hierarchies in RITL and cognition generally

Flexibly adapting to emotional cues: Examining the functional and structural correlates of emotional reactivity and emotion control in healthy and depressed individuals

The ability of emotionally significant stimuli to bias our behaviour is an evolutionarily adaptive phenomenon. However, sometimes emotions become excessive, inappropriate, and even pathological, like in major depressive disorder (MDD). Emotional flexibility includes both the neural processes involved in reacting to, or representing, emotional significance, and those involved in controlling emotional reactivity. MDD represents a potentially distinct form of emotion (in)flexibility, and therefore offers a unique perspective for understanding both the integration of conflicting emotional cues and the neural regions involved in actively controlling emotional systems. The present investigation of emotional flexibility began by considering the functional neural correlates of competing socio-emotional cues and effortful emotion regulation in MDD using both negative and positive emotions. Study 1 revealed greater amygdala activity in MDD relative to control participants when negative cues were centrally presented and task-relevant. No significant between-group differences were observed in the amygdala for peripheral task-irrelevant negative distracters. However, controls demonstrated greater recruitment of the ventrolateral (vlPFC) and dorsomedial prefrontal cortices (dmPFC) implicated in emotion control. Conversely, attenuated amygdala activity for task-relevant and irrelevant positive cues was observed in depressed participants. In Study 2, effortful emotion regulation using strategies adapted from cognitive behaviour therapy (CBT) revealed greater activity in regions of the dorsal and lateral prefrontal cortices in both MDD and control participants when attempting to either down-regulate negative or up-regulate positive emotions. During the down-regulation of negative cues, only controls displayed a significant reduction of amygdala activity. In Study 3, an individual differences approach using multiple regression revealed that while greater amygdala-vmPFC structural connectivity was associated with low trait-anxiety, greater connectivity between amygdala and regions of occipitotemporal and parietal cortices was associated with high trait-anxiety. These findings are discussed with respect to current models of emotional reactivity and emotion control derived from studies of both healthy individuals and those with emotional disorders, particularly depression. The focus is on amygdala variability in differing contexts, the role of the vmPFC in the modulation of amygdala activity via learning processes, and the modulation of emotion by attention or cognitive control mechanisms initiated by regions of frontoparietal cortices