Advances In Neuroscience Research Articles

Role of Artificial Intelligence in Detecting Neurological Disorders

AI plays a pivotal role in detecting neurological disorders by leveraging advanced technologies to analyze vast amounts of data and aid in diagnosis. Here are several key roles AI plays. Artificial Intelligence (AI) has emerged as a revolutionary tool in the realm of healthcare, particularly in the early detection and accurate diagnosis of neurological disorders. The present paper delves into the multifaceted applications of AI specifically tailored to identify and discern various neurological conditions. AI's prowess in medical imaging analysis has significantly advanced the field by enabling nuanced and precise identification of neurological anomalies. By meticulously analyzing MRI scans, CT scans, and X-rays, AI-driven algorithms excel in detecting subtle patterns indicative of diverse neurological disorders such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and brain tumors. These technologies not only enhance diagnostic accuracy but also enable early intervention and improved patient outcomes. Moreover, AI leverages extensive datasets encompassing clinical records, genetic information, and biosensor data to predict and assess an individual's susceptibility to neurological disorders. Predictive analytics powered by machine learning models, aid in risk assessment, paving the way for personalized medicine and proactive healthcare strategies. Ethical considerations underscore the implementation of AI in neurological disorder detection, emphasizing the need for transparent algorithms, stringent data privacy protocols, and unbiased AI systems to ensure patient confidentiality and trust in healthcare. The evolving landscape of AI in neuroscience presents an exciting frontier, fostering collaborations between AI experts and neuroscientists. Together, they aim to unravel the intricacies of neurological disorders, pushing the boundaries of innovation and paving the path toward early detection, targeted treatments, and improved quality of life for individuals affected by these conditions. This paper highlights the transformative impact of AI in detecting neurological disorders, 7emphasizing its role in early detection, personalized medicine, ethical considerations, and the potential for collaborative advancements in neuroscience.

Per Andersen. 12 January 1930—17 February 2020

Per Andersen was one of the leading neuroscientists of the second half of the twentieth century. He spent his entire career at the University of Oslo, apart from an exceptionally productive postdoctoral period with Sir John Eccles FRS at the Australian National University in Canberra between 1961 and 1963. As a PhD student he laid the foundation of field potential analysis, which allowed synaptic function in laminated cortical structures such as the hippocampus to be followed for long periods in intact animals, an essential technical prerequisite for the study of the synaptic basis of memory. In Canberra he identified the neurons responsible for feed-back inhibition in the hippocampus and feed-forward inhibition in the cerebellum, the first inhibitory neurons to be functionally identified in the mammalian brain. Per's Canberra achievements catapulted him to superstar status, and enabled him on his return to Oslo in 1963 to establish a laboratory that played a major role over the following decades in characterizing the functional properties of hippocampal synapses. From his laboratory emerged a string of significant advances, including the discovery of long-term potentiation, a form of synaptic plasticity widely believed to support learning and memory, and the introduction of the transverse hippocampal slice, which rapidly became the dominant preparation for investigating the properties of hippocampal neurons. Young researchers, many from the USA and Canada, flocked to his laboratory for postdoctoral training, adding to the steady stream of Norwegian doctoral students, two of whom went on to win the Nobel Prize. Per was also committed to the importance of communicating scientific advances to the general public, and contributed prolifically over the years to the Norwegian media, commentating on advances in neuroscience and their relevance to neurological disorders.

Free will: An Example of the Dopaminergic System.

Neuroscience has convinced people that much of their behavior is determined by causes unknown to them and beyond their control. However, are advances in neuroscience truly a prerequisite for such beliefs? Robert Kane's theory of ultimate responsibility is libertarian theory. Its innovative nature makes it possible to discuss the neurophysiological basis of its postulates. Using the functions of the midbrain dopaminergic system as an example, this article provides an overview of this neurophysiological basis. According to Kane, if we are to be ultimately responsible for our wills as well as for our actions, some actions in our lives must lack sufficient motives and causes. These are self-forming actions. Dopamine is hypothesized to mediate self-forming action execution. Dopamine not only mediates action but also ensures synaptic plasticity in the brain, that is, learning from action; hence, dopamine changes the acting individual and provides the formation of our own wills. The basal ganglia, which are the main target of dopamine in the brain, act through parallel pathways and are involved in decision-making processes. Dopamine is also involved in the regulation of the neurodynamical properties of prefrontal cortex networks with random spiking noise. It can be assumed that the activity of the dopaminergic system is closely related to the physiological basis of free will.

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

Neuroscience Contribution in Educational Leadership. Challenges and Perspectives

Education is founded upon the instruction process and its outcomes, commonly called learning outcomes. The underlying foundation of this entire process is the brain. Neuroscience encompasses the study of brain function, including the intricate processes involved in human learning. It also investigates the factors that contribute to variations in learning abilities among individuals. Therefore, it is unsurprising that individuals engaged in educational science are highly interested in integrating the discoveries of neurosciences with educational theories of learning. Neuroscientific research has been conducted for approximately three decades. However, there has been a growing interest in integrating neuroscience and education in recent years. This is evident from the numerous articles published in books and scientific journals that explore the relationship between these two fields. Additionally, the integration of neuroscience into educational leadership presents both opportunities and challenges. Educational neuroscience posits that insights from neuroscience can enhance teaching in the classroom. However, there are practical and principled issues associated with educational neuroscience, which need to be addressed. Neuroscience is revealing to educational leadership that it is a social process, with the human brain being a social organ interacting with others in social contexts. Incorporating neuroscience into educational leadership has the potential to increase its effectiveness, but contradictory studies address the critical nature of this integration. The application of advances in psychology, behavioral economics, and cognitive neuroscience to study school leaders’ decision-making processes is a tantalizing area. Finally, it is essential that neuroscientific data and interpretations are rooted in education to be embraced by the education profession.

Neural specialization with generalizable representations underlies children's cognitive development of attention.

From childhood to adulthood, the human brain develops highly specialized yet interacting neural modules that give rise to nuanced attention and other cognitive functions. Each module can specialize over development to support specific functions, yet also coexist in multiple neurobiological modes to support distinct processes. Advances in cognitive neuroscience have conceptualized human attention as a set of cognitive processes anchored in highly specialized yet interacting neural systems. The underlying mechanisms of how these systems interplay to support children's cognitive development of multiple attention processes remain unknown. Leveraging developmental functional magnetic resonance imaging with attention network test paradigm, we demonstrate differential neurocognitive development of three core attentional processes from childhood to adulthood, with alerting reaching adult-like level earlier, followed by orienting and executive attention with more protracted development throughout middle and late childhood. Relative to adults, young children exhibit immature specialization with less pronounced dissociation of neural systems specific to each attentional process. Children manifest adult-like distributed representations in the ventral attention and cingulo-opercular networks, but less stable and weaker generalizable representations across multiple processes in the dorsal attention network. Our findings provide insights into the functional specialization and generalization of neural representations scaffolding cognitive development of core attentional processes from childhood to adulthood. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neuroscience of Hypnosis: A Neurophysiological View of Hypnotherapy

Objective: This narrative literature review is intended to briefly clarify the history and concepts of hypnosis, history, scientific advancement in the field and therapeutic applications. The theme will have greater contextualization regarding the neurophysiology of hypnosis. Methods: In this bibliographic review (databases, websites and specialized journals) studies were selected seeking as keywords "Hypnosis", "Neurobiology", "Hypnotic Response", "Historical", "Research", to approach these concepts in the clinical area and research in the health area, in order to seek greater grounding on the concepts and neurobiological mechanisms involved in hypnotherapy. The databases evaluated were MEDLINE, GOOGLE ACADEMIC, PUBMED, SCIENCE DIRECT, SciELO and LILACS. Results: In the selected studies on the theme, the concepts on hypnotherapy and history are addressed. Hypnosis, recognized as a psychobiological process, modifies brain activity, affecting areas such as sensory cortexes, the dorsal anterior cingulate gyrus, and the prefrontal cortex. This neurocognitive alteration influences perception, making hypnosis an effective tool in the modulation of various clinical conditions in a way that complements conventional therapy. Conclusion: The neurophysiology of hypnosis is a field in constant evolution. Although the exact understanding of the neurophysiological processes underlying hypnosis is not yet fully understood, advances in neuroscience reveal insights into the hypnotic response. The progress in new neuroimaging technologies and methodologies may provide clearer answers about the mechanisms involved in hypnotherapy, as well as proving the effectiveness of its application.

What is the most important innovation in neurosurgical practice that will emerge in the next 10 years?

Brain computer interfaces BCIs are automated neurostimulators that analyse brain signals and translate them into output. They aim to allow modulation of neurological functioning at cellular and network levels without the need for clinician-performed adjustments. BCIs are beginning to progress from the research and development phase to their introduction into clinical neurosurgery. Some anticipated applications include advanced treatment of cerebral palsy and stroke, aiming to restore function to these patients. In time, they will become one of the most significant innovations in neurosurgery. 
 Provided here is a review of BCI research to date and their implementation into clinical neuroscience, aiming to highlight the importance of BCIs as a developing treatment modality in neurosurgery in the next decade. 
 A review of the literature was performed using Pubmed as the primary database. BCI applications, recent technological advancements and current challenges facing these devices were reviewed. Trends in research and development were considered and are discussed. It was found that significant developments have been made in manufacturing BCIs that can interpret cortical processes with adequate spatial and temporal resolution. It was shown that the number of procedures to implant neuromodulators taking place in the UK each year is increasing, with more funding provided for BCI research each year. Further to this, a review of publication data found that neuromodulation devices placed among the top new patent categories in neurosurgery in recent years, a large portion of which were BCIs. These results highlight the continually growing interest in BCI research and their application in neurosurgery. 
 In conclusion, BCIs will become an increasingly important area of innovation in neurosurgery. Advances in translational neuroscience, imaging technologies and signal analysis will provide clearer insights into disease processes, allowing increasingly complex devices to address current limitations in the treatment of these conditions.

Beta bursts question the ruling power for brain–computer interfaces

Objective: Current efforts to build reliable brain–computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in ‘left vs. right hand’ motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography

Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers’ drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, being also necessary to study the performance of scalable ML models suitable for groups of subjects. To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. Results show that Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated. Thus, the proposed framework presents a novel approach for detecting drowsiness in driving scenarios using BCIs and ML.

Targeting Human Glucocorticoid Receptors in Fear Learning: A Multiscale Integrated Approach to Study Functional Connectivity.

Fear extinction is a phenomenon that involves a gradual reduction in conditioned fear responses through repeated exposure to fear-inducing cues. Functional brain connectivity assessments, such as functional magnetic resonance imaging (fMRI), provide valuable insights into how brain regions communicate during these processes. Stress, a ubiquitous aspect of life, influences fear learning and extinction by changing the activity of the amygdala, prefrontal cortex, and hippocampus, leading to enhanced fear responses and/or impaired extinction. Glucocorticoid receptors (GRs) are key to the stress response and show a dual function in fear regulation: while they enhance the consolidation of fear memories, they also facilitate extinction. Accordingly, GR dysregulation is associated with anxiety and mood disorders. Recent advancements in cognitive neuroscience underscore the need for a comprehensive understanding that integrates perspectives from the molecular, cellular, and systems levels. In particular, neuropharmacology provides valuable insights into neurotransmitter and receptor systems, aiding the investigation of mechanisms underlying fear regulation and potential therapeutic targets. A notable player in this context is cortisol, a key stress hormone, which significantly influences both fear memory reconsolidation and extinction processes. Gaining a thorough understanding of these intricate interactions has implications in terms of addressing psychiatric disorders related to stress. This review sheds light on the complex interactions between cognitive processes, emotions, and their neural bases. In this endeavor, our aim is to reshape the comprehension of fear, stress, and their implications for emotional well-being, ultimately aiding in the development of therapeutic interventions.

Mind and Machine: A Philosophical Examination of Matt Carter’s “Minds & Computers: An Introduction to the Philosophy of Artificial Intelligence”

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural languag processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. This book provides critical insights for researchers in AI and cognitive science, motivating further inquiry into the philosophical foundations of artificial intelligence and its practical implications.

Analysis of the brain’s responses to the approach-avoidance behavior in architectural experience

Creating an optimal environment that addresses the users? psychological and behavioral needs requires a deep understanding of mental processes. Advances in neuroscience and applying its technical methods, such as neuroimaging, have provided the possibility to make an objective exploration of how architecture affects users? brain reactions. This study aims to objectify the effects of spatial characteristics of architectural settings on the brain?s responses and the users? approach-avoidance behavior. Therefore, to determine the regional activation of the nervous system responsible for emotions and approach-avoidance behavior, participants? brains were scanned with functional magnetic resonance imaging while looking at photographs of architectural spaces. Results showed that the pleasant and unpleasant spaces activate the brain structures involved in positive and negative emotions. Additionally, pleasant and unpleasant spaces were more likely to elicit inter and exit decisions and activate the brain?s regions engaged in approach and avoidance decisions.

Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images.

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

Integrative learning in the lens of meta-learned models of cognition: Impacts on animal and human learning outcomes

Abstract This commentary examines the synergy between meta-learned models of cognition and integrative learning in enhancing animal and human learning outcomes. It highlights three integrative learning modes – holistic integration of parts, top-down reasoning, and generalization with in-depth analysis – and their alignment with meta-learned models of cognition. This convergence promises significant advances in educational practices, artificial intelligence, and cognitive neuroscience, offering a novel perspective on learning and cognition.

Biomimetic learning of hand gestures in a humanoid robot.

Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.

Bilinç Bilmecesinin Kısa Bir Analizi

The challenging problem of consciousness, or more traditionally the mind-body problem, continues to maintain its vitality and importance despite all philosophical theories and advances in neuroscience, artificial intelligence and technology. The problem of consciousness has been addressed by both philosophers and scientists from ancient times to the present. In the most general sense, the problem of consciousness, also known as the hard problem, is the problem of how subjective mental states arise from a physical piece of meat. The main purpose of this article is to present a categorical and explanatory analysis of this puzzle of consciousness, which is still important today. For this purpose, first of all, the definitions of the concept of consciousness in the literature will be examined in depth. Afterwards, the characteristics of consciousness that are untamed for science, which turns it into the hard problem for us, will be touched upon, and the puzzle of consciousness will be explained with Chalmers' distinction between easy problems and the hard problem of consciousness. Finally, it is aimed to complete this explanatory analysis on the puzzle of consciousness by making a categorical distinction between traditional solution-oriented approaches to the problem of consciousness.

Gestalt's Perspective on Insight: A Recap Based on Recent Behavioral and Neuroscientific Evidence.

The Gestalt psychologists' theory of insight problem-solving was based on a direct parallelism between perceptual experience and higher-order forms of cognition (e.g., problem-solving). Similarly, albeit not exclusively, to the sudden recognition of bistable figures, these psychologists contended that problem-solving involves a restructuring of one's initial representation of the problem's elements, leading to a sudden leap of understanding phenomenologically indexed by the "Aha!" feeling. Over the last century, different scholars have discussed the validity of the Gestalt psychologists' perspective, foremost using the behavioral measures available at the time. However, in the last two decades, scientists have gained a deeper understanding of insight problem-solving due to the advancements in cognitive neuroscience. This review aims to provide a retrospective reading of Gestalt theory based on the knowledge accrued by adopting novel paradigms of research and investigating their neurophysiological correlates. Among several key points that the Gestalt psychologists underscored, we focus specifically on the role of the visual system in marking a discrete switch of knowledge into awareness, as well as the perceptual experience and holistic standpoints. While the main goal of this paper is to read the previous theory in light of new evidence, we also hope to initiate an academic discussion and encourage further research about the points we raise.

Efficient functional localization of language regions in the brain

Important recent advances in the cognitive neuroscience of language have been made using functional localizers to demarcate language-selective regions in individual brains. Although single-subject localizers offer insights that are unavailable in classic group analyses, they require additional scan time that imposes costs on investigators and participants. In particular, the unique practical challenges of scanning children and other special populations has led to less adoption of localizers for neuroimaging research with these theoretically and clinically important groups. Here, we examined how measurements of the spatial extent and functional response profiles of language regions are affected by the duration of an auditory language localizer. We compared how parametrically smaller amounts of data collected from one scanning session affected (i) consistency of group-level whole-brain parcellations, (ii) functional selectivity of subject-level activation in individually defined functional regions of interest (fROIs), (iii) sensitivity and specificity of subject-level whole-brain and fROI activation, and (iv) test-retest reliability of subject-level whole-brain and fROI activation. For many of these metrics, the localizer duration could be reduced by 50-75% while preserving the stability and reliability of both the spatial extent and functional response profiles of language areas. These results indicate that, for most measures relevant to cognitive neuroimaging studies, the brain's language network can be localized just as effectively with 3.5 min of scan time as it can with 12 min. Minimizing the time required to reliably localize the brain's language network allows more effective localizer use in situations where each minute of scan time is particularly precious.

Modeling Brain Representations of Words' Concreteness in Context Using GPT-2 and Human Ratings.

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.