A brief history of human brain mapping

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

Transforming Neurology and Psychiatry: Organ-specific PET Instrumentation and Clinical Applications

PET technology has immense potential for furthering understanding of the brain and associated disorders, including advancements in high-resolution tomographs and hybrid imaging modalities. Novel radiotracers targeting specific neurotransmitter systems and molecular markers provide opportunities to unveil intricate mechanisms underlying neurologic and psychiatric conditions. As PET imaging techniques and analysis methods continue to be refined, the field is poised to make significant contributions to personalized medicine for more targeted and effective interventions. PET instrumentation has advanced the fields of neurology and psychiatry, providing insights into pathophysiology and development of effective treatments.

Plant Nutrition-New Methods Based on the Lessons of History: A Review

As with new technologies, plant nutrition has taken a big step forward in the last two decades. The main objective of this review is to briefly summarise the main pathways in modern plant nutrition and attract potential researchers and publishers to this area. First, this review highlights the importance of long-term field experiments, which provide us with valuable information about the effects of different applied strategies. The second part is dedicated to the new analytical technologies (tomography, spectrometry, and chromatography), intensively studied environments (rhizosphere, soil microbial communities, and enzymatic activity), nutrient relationship indexes, and the general importance of proper data evaluation. The third section is dedicated to the strategies of plant nutrition, i.e., (i) plant breeding, (ii) precision farming, (iii) fertiliser placement, (iv) biostimulants, (v) waste materials as a source of nutrients, and (vi) nanotechnologies. Finally, the increasing environmental risks related to plant nutrition, including biotic and abiotic stress, mainly the threat of soil salinity, are mentioned. In the 21st century, fertiliser application trends should be shifted to local application, precise farming, and nanotechnology; amended with ecofriendly organic fertilisers to ensure sustainable agricultural practices; and supported by new, highly effective crop varieties. To optimise agriculture, only the combination of the mentioned modern strategies supported by a proper analysis based on long-term observations seems to be a suitable pathway.

An Umbrella Review of the Fusion of fMRI and AI in Autism

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study's objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation

Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.

Complex of global functional network as the core of consciousness

Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.

Mesoscopic in vivo human T2* dataset acquired using quantitative MRI at 7 Tesla

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T₂^* has not been performed. Here we provide a dataset containing empirical T₂^* measurements acquired at 0.35 × 0.35 × 0.35 mm³ voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T₂^* (as well as R₂^*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T₂^*-weighted (f)MRI signal.

X marks the spot: mapping the brain with EEG and MRI

In this month's Technology News, we go back to the basics of two neuroimaging techniques and look at how these have been advanced in 2022.

Mapping oxidative metabolism in the human brain with calibrated fMRI in health and disease

Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMR_O2) to support brain function. Dynamic CMR_O2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this "calibration" is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMR_O2 changes. However non-human studies have demonstrated that the "calibration" can also be achieved without gases, revealing good agreement between CMR_O2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Causal mapping of human brain function

Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.

Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterization of the human brain. How these 2 modalities differ in their contribution to cognition and behavior is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that although for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, whereas for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.

Overview of (f)MRI Studies of Cognitive Aging for Non-Experts: Looking through the Lens of Neuroimaging

This special issue concerning Brain Functional and Structural Connectivity and Cognition aims to expand our understanding of brain connectivity. Herein, I review related topics including the principle and concepts of functional MRI, brain activation, and functional/structural connectivity in aging for uninitiated readers. Visuospatial attention, one of the well-studied functions in aging, is discussed from the perspective of neuroimaging.

Circuit-Targeted Neuromodulation for Anhedonia

Despite the prevalence of anhedonia across multiple psychiatric disorders, its relevance to treatment selection and prognostication can be unclear (Davey et al., Psychol Med 42(10):2071-81, 2012). Given the challenges in pharmacological and psychosocial treatment, there has been increasing attention devoted to neuroanatomically-targeted treatments. This chapter will present a brief introduction to circuit-targeted therapeutics in psychiatry (Sect. 1), an overview of brain mapping as it relates to anhedonia (Sect. 2), a review of existing studies on brain stimulation for anhedonia (Sect. 3), and a description of emerging approaches to circuit-based neuromodulation for anhedonia (Sect. 4).

Quantitation of cerebral oxygen tension using phasor analysis and phosphorescence lifetime imaging microscopy (PLIM)

Time-domain measurements for fluorescence lifetime imaging microscopy (FLIM) and phosphorescence lifetime imaging microscopy (PLIM) are conventionally computed by nonlinear curve fitting techniques to model the time-resolved profiles as mono- or multi-exponential decays. However, these techniques are computationally intensive and prone to fitting errors. The phasor or "polar plot" analysis method has recently gained attention as a simple method to characterize fluorescence lifetime. Here, we adapted the phasor analysis method for absolute quantitation of phosphorescence lifetimes of oxygen-sensitive phosphors and used the phasor-derived lifetime values to quantify oxygen partial pressure (pO2) in cortical microvessels of awake mice. Our results, both experimental and simulated, demonstrate that oxygen measurements obtained from computationally simpler phasor analysis agree well with traditional curve fitting calculations. To our knowledge, the current study constitutes the first application of the technique for characterizing microsecond-length, time-domain phosphorescence measurements and absolute, in vivo quantitation of a vital physiological parameter. The method shows promise for monitoring cerebral metabolism and pathological changes in preclinical rodent models.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

Virtual Reality for Neurorehabilitation and Cognitive Enhancement

Our access to computer-generated worlds changes the way we feel, how we think, and how we solve problems. In this review, we explore the utility of different types of virtual reality, immersive or non-immersive, for providing controllable, safe environments that enable individual training, neurorehabilitation, or even replacement of lost functions. The neurobiological effects of virtual reality on neuronal plasticity have been shown to result in increased cortical gray matter volumes, higher concentration of electroencephalographic beta-waves, and enhanced cognitive performance. Clinical application of virtual reality is aided by innovative brain–computer interfaces, which allow direct tapping into the electric activity generated by different brain cortical areas for precise voluntary control of connected robotic devices. Virtual reality is also valuable to healthy individuals as a narrative medium for redesigning their individual stories in an integrative process of self-improvement and personal development. Future upgrades of virtual reality-based technologies promise to help humans transcend the limitations of their biological bodies and augment their capacity to mold physical reality to better meet the needs of a globalized world.

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

The Impact of Lithium on Brain Function in Bipolar Disorder: An Updated Review of Functional Magnetic Resonance Imaging Studies

Lithium remains a gold standard treatment for bipolar disorder (BD), and functional magnetic resonance imaging (fMRI) studies have contributed to clarifying its impact on neural circuitries in affected individuals. However, the specific neurobiological mechanisms through which lithium exerts its effects on brain function are not fully understood. In this review, we aimed to summarize the results of recent fMRI studies evaluating the impact of lithium on brain functional activity and connectivity in patients diagnosed with BD. We performed a literature search of available sources found in the PubMed database reported in English since 2016, when the last available review on this topic was published. Five fMRI studies in resting-state condition and six studies performed during the execution of emotional tasks met the inclusion criteria. Overall, the available evidence supports normalizing effects of lithium on brain activity and connectivity. Most of these studies reported a normalization in prefrontal regions and interconnected areas involved in emotion regulation and processing, regardless of the task employed. Importantly, lithium treatment showed distinct patterns of activity/connectivity changes compared with other treatments. Finally, lithium modulation of neural circuitries was found to be associated with clinical improvement in BD. These results are consistent with the hypothesis that selective abnormalities in neural circuitries supporting emotion processing and regulation improve during lithium treatment in BD. However, the heterogeneity of the examined studies regarding study design, sample selection, and analysis methods might limit the generalizability of the findings and lead to difficulties in comparing the results. Therefore, in future studies, larger cohorts and homogeneous experimental tasks are needed to further corroborate these findings.

Homotopic functional connectivity disruptions in glioma patients are associated with tumor malignancy and overall survival

Background

Gliomas exhibit widespread bilateral functional connectivity (FC) alterations that may be associated with tumor grade. Limited studies have examined the connection-level mechanisms responsible for these effects. Given the typically strong FC observed between mirroring/homotopic brain regions in healthy subjects, we hypothesized that homotopic connectivity (HC) is altered in low-grade and high-grade glioma patients and the extent of disruption is associated with tumor grade and predictive of overall survival (OS) in a cohort of de novo high-grade glioma (World Health Organization [WHO] grade 4) patients.

Methods

We used a mirrored FC-derived cortical parcellation to extract blood-oxygen-level-dependent (BOLD) signals and to quantify FC differences between homotopic pairs in normal-appearing brain in a retrospective cohort of glioma patients and healthy controls.

Results

Fifty-nine glioma patients (WHO grade 2, n = 9; grade 4 = 50; mean age, 57.5 years) and 30 healthy subjects (mean age, 65.9 years) were analyzed. High-grade glioma patients showed lower HC compared with low-grade glioma patients and healthy controls across several cortical locations and resting-state networks. Connectivity disruptions were also strongly correlated with hemodynamic lags between homotopic regions. Finally, in high-grade glioma patients with known survival times (n = 42), HC in somatomotor and dorsal attention networks were significantly correlated with OS.

Conclusions

These findings demonstrate an association between tumor grade and HC alterations that may underlie global FC changes and provide prognostic information.

Neuroimaging in Pediatric Patients with Mild Traumatic Brain Injury: Relating the Current 2018 Centers for Disease Control Guideline and the Potential of Advanced Neuroimaging Modalities for Research and Clinical Biomarker Development

The Center for Disease Control and Prevention (CDC)'s 2018 Guideline for current practices in pediatric mild traumatic brain injury (mTBI; also referred to as concussion herein) systematically identified the best up-to-date practices based on current evidence and, specifically, identified recommended practices regarding computed tomography (CT), magnetic resonance imaging (MRI), and skull radiograph imaging. In this article, we discuss types of neuroimaging not discussed in the guideline in terms of their safety for pediatric populations, their potential application, and the research investigating the future use of certain modalities to aid in the diagnosis and treatment of mTBI in children. The role of neuroimaging in pediatric mTBI cases should be considered for the potential contribution to children's neural and social development, in addition to the immediate clinical value (as in the case of acute structural findings). Selective use of specific neuroimaging modalities in research has already been shown to detect aspects of diffuse brain injury, disrupted cerebral blood flow, and correlate physiological factors with persistent symptoms, such as fatigue, cognitive decline, headache, and mood changes, following mTBI. However, these advanced neuroimaging modalities are currently limited to the research arena, and any future clinical application of advanced imaging modalities in pediatric mTBI will require robust evidence for each modality's ability to provide measurement of the subtle conditions of brain development, disease, damage, or degeneration, while accounting for variables at both non-injury and time-post-injury epochs. Continued collaboration and communication between researchers and healthcare providers is essential to investigate, develop, and validate the potential of advanced imaging modalities in pediatric mTBI diagnostics and management.

Quantitative assessment of optical coherence tomography angiography algorithms for neuroimaging

Optical coherence tomography (OCT) angiography can noninvasively map microvascular networks and quantify blood flow in a cerebral cortex with a resolution of 1 to 10 μm and a penetration depth of 2 to 3 mm incorporating OCT signals and angiography algorithms. Different angiography algorithms have been developed in recent years; however, the performance of the algorithms has not been assessed quantitatively for neuroimaging applications. In this paper, we developed four metrics including vascular connectivity, contrast-to-noise ratio, signal-to-noise ratio and processing time to quantitatively assess the performance of OCT angiography algorithms in image quality and computation speed. After the imaging of a rat cortex using an OCT system, the cerebral microvascular networks were visualized by seven algorithms, and the performance of the algorithms was quantified and compared. Quantitative performance assessment of the algorithms can provide suggestions for the selection of appropriate OCT angiography algorithms in neuroimaging.

Spatiotemporal neuroscience - what is it and why we need it

The excellent commentaries to our target paper hint upon three main issues, (i) spatiotemporal neuroscience; (ii) neuro-mental relationship; and (iii) mind, brain, and world relationship. (i) We therefore discuss briefly the history of Spatiotemporal Neuroscience. Distinguishing it from Cognitive Neuroscience and related branches (like Affective, Social, etc. Neuroscience), Spatiotemporal Neuroscience can be characterized by focus on brain activity (rather than brain function), spatiotemporal relationship (rather than input-cognition-output relationship), and structure (rather than stimuli/contents). (ii) Taken in this sense, Spatiotemporal Neuroscience allows one to conceive the neuro-mental relationship in dynamic spatiotemporal terms that complement and extend (rather than contradict) their cognitive characterization. (iii) Finally, more philosophical issues like the need to dissolve the mind-body problem (and replace it by the world-brain relation) and the question for different levels of time including their nestedness are discussed.

Neurodevelopmental and cognitive disabilities: Historical overview

This chapter explores the history of the developmental sciences and their impact on the study of neurodevelopmental and cognitive disabilities from the start of the 20th century to the present day. It covers the origins of intelligence testing and developmental schedules and the importance of early laboratory testing on identifying the causes of developmental differences. It also explores the importance of major legal and institutional changes after the Second World War in reframing approaches to developmental conditions. Postwar attempts to limit institutional care and to improve educational services had a significant influence on the growth of the cognitive science movement and genetic studies in the 1960s. The chapter argues that this history is critical to understanding contemporary approaches to neurodevelopmental science. Historical investigation demonstrates the complex ways that technological, social, and political changes can directly impact medical and scientific practice. It can therefore play an important role in informing those practices in the present.

Mapping the human middle longitudinal fasciculus through a focused anatomo-imaging study: shifting the paradigm of its segmentation and connectivity pattern

Τhe middle longitudinal fasciculus (MdLF) was initially identified in humans as a discrete subcortical pathway connecting the superior temporal gyrus (STG) to the angular gyrus (AG). Further anatomo-imaging studies, however, proposed more sophisticated but conflicting connectivity patterns and have created a vague perception on its functional anatomy. Our aim was, therefore, to investigate the ambiguous structural architecture of this tract through focused cadaveric dissections augmented by a tailored DTI protocol in healthy participants from the Human Connectome dataset. Three segments and connectivity patterns were consistently recorded: the MdLF-I, connecting the dorsolateral Temporal Pole (TP) and STG to the Superior Parietal Lobule/Precuneus, through the Heschl's gyrus; the MdLF-II, connecting the dorsolateral TP and the STG with the Parieto-occipital area through the posterior transverse gyri and the MdLF-III connecting the most anterior part of the TP to the posterior border of the occipital lobe through the AG. The lack of an established termination pattern to the AG and the fact that no significant leftward asymmetry is disclosed tend to shift the paradigm away from language function. Conversely, the theory of "where" and "what" auditory pathways, the essential relationship of the MdLF with the auditory cortex and the functional role of the cortical areas implicated in its connectivity tend to shift the paradigm towards auditory function. Allegedly, the MdLF-I and MdLF-II segments could underpin the perception of auditory representations; whereas, the MdLF-III could potentially subserve the integration of auditory and visual information.

Artificial intelligence for precision medicine in neurodevelopmental disorders

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.

Use of functional magnetic resonance imaging in patients with irritable bowel syndrome and functional dyspepsia

Functional brain imaging (positron emission tomography – PET, functional magnetic resonance imaging – fMRI), allowing in vivo analysis of the brain-digestive tract interaction and the neurological mechanisms underlying visceral hypersensitivity, significantly advanced research and helped in the understanding of the interrelations in this field. Differences in this parameter can result from alterations in task-related cognitive states or from resting state processes. Nowadays, advanced imaging techniques such as fMRI are more frequently used and are acknowledged among both clinicians and radiologists in the diagnostic algorithm of digestive tract diseases. Functional dyspepsia is a condition in which neuroimaging allows for analysis of dysfunctions within the brain-gut axis (BGA) engaged in processing of visceral discomfort and pain. The results of studies in patient groups with irritable bowel syndrome prove that psychosocial factors significantly affect the mechanisms regulating visceral sensitivity within the brain. The BGA includes neuronal pathways (autonomic nervous system), neuroendocrine (hypothalamo-pituitary-adrenal axis), and neuroimmunological ones. Psychological processes affect the functioning of the digestive system and can cause dyspeptic symptoms. A patient’s mental condition associated with stress can affect processes taking place in the central nervous system and trigger somatic reactions in the digestive tract through the autonomic visceral system.

Introduction to a Special Issue on Innovations and Controversies in Brain Imaging of Pain: Methods and Interpretations

This special issue comprised 14 articles from leaders in the field, that provide opinions and reviews of concepts that are central to the next generation of pain imaging studies. Topics include cutting-edge technologies and approaches that are at the forefront of such studies, as well as developments toward biomarkers of pain and clinical applications that bring us closer to harnessing understanding of pains and its modulation to offer better options to those suffering from pain.

Reprint of: F-18Fluorodeoxyglucose positron emission tomography studies of the schizophrenia spectrum: The legacy of Monte S. Buchsbaum, M.D

This is a selective review of the work of Buchsbaum and colleagues. It revisits and pays tribute to four decades of publications employing positron emission tomography (PET) with ^F-18fluorodeoxyglucose (FDG) to examine the neurobiology of schizophrenia-spectrum disorders (including schizotypal personality disorder (SPD) and schizophrenia). Beginning with a landmark FDG-PET study in 1982 reporting hypofrontality in unmedicated schizophrenia patients, Buchsbaum and colleagues published high-impact work on regional glucose metabolic rate (GMR) abnormalities in the spectrum. Several key discoveries were made, including the delineation of schizophrenia-spectrum abnormalities in frontal and temporal lobe, cingulate, thalamus, and striatal regions using three-dimensional mapping with coregistered MRI and PET. These findings indicated that SPD patients have less marked frontal lobe and striatal dysfunction compared with schizophrenia patients, possibly mitigating frank psychosis. Additionally, these investigations were among the first to conduct early seed-based functional connectivity analyses with FDG-PET, showing aberrant cortical-subcortical circuitry and, in particular, revealing a thalamocortical circuitry abnormality in schizophrenia. Finally, pioneering work employing the first double-blind randomized antipsychotic (haloperidol) vs. placebo FDG-PET study design in schizophrenia indicated that GMR in the striatum, more than in any other region, was related to clinical response.

A system for collecting and analyzing experience-sampling data

Online and sensor technologies promise to transform many areas of psychological enquiry. However, collecting and analyzing such data are challenging. In this article, we introduce the unforgettable.me experience-sampling platform. Unforgettable.me includes an app that can collect image, Global Positioning System, accelerometry, and audio data in a continuous fashion and upload the data to a server. The data are then automatically augmented by using online databases to identify the address, type of location, and weather conditions, as well as provide street view imagery. In addition, machine-learning classifiers are run to identify aspects of the audio data such as voice and traffic. The augmented data are available to participants in the form of a keyword search interface, as well as via several visualization mechanisms. In addition, Unforgettable Research Services partners with If This Then That (IFTTT), and so can accumulate data from any of over 600 sources, including social media, wearables, and other devices. Through IFTTT, buttons can be added as icons to smartphones to allow participants to register mood conveniently, as well as behaviors and physiological states such as happiness, microaggressions, or illness. Furthermore, unforgettable.me incorporates a mechanism that allows researchers to run experiments and analyze data within an authenticated environment without viewing users' private data.

Is temporo-spatial dynamics the "common currency" of brain and mind? In Quest of "Spatiotemporal Neuroscience"

Neuroscience has made considerable progress in unraveling the neural correlates of mental phenomena like self, consciousness, and perception. However, the "common currency" shared between neuronal and mental activity, brain and mind, remains yet unclear. In this article, we propose that the dynamics of time and space provides a "common currency" that connects neuronal and mental features. Time and space are here understood in a dynamic context (as in contemporary physics): that is, in terms of the way the brain's spontaneous activity constructs its spatial and temporal relationships, for instance in terms of functional connectivity and different frequencies of fluctuations. Recruiting recent empirical evidence, we show that the different ways in which the spontaneous activity constructs its "inner time and space" are manifested in distinct mental features. Specifically, we demonstrate how spatiotemporal mechanisms like spatiotemporal repertoire, integration, and speed yield mental features like consciousness, self, and time speed perception. The focus on the brain's spatiotemporal mechanisms entails what we describe as "Spatiotemporal Neuroscience". Spatiotemporal Neuroscience conceives neuronal activity in terms of its temporo-spatial dynamics rather than its various functions (e.g., cognitive, affective, social, etc.) as in other branches of neuroscience (as distinguished from Cognitive, Affective, Cultural, Social, etc. Neuroscience). That allows Spatiotemporal Neuroscience to take into view the so-called 'spatio-temporality' of mental features including their non-causal, intrinsic and transformative relationship with neuronal features. In conclusion, Spatiotemporal Neuroscience opens the door to investigate and ultimately reveal the brain's own temporo-spatial dynamics as the hitherto missing "common currency" of neuronal and mental features.

Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging

The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.

F-18Fluorodeoxyglucose positron emission tomography studies of the schizophrenia spectrum: The legacy of Monte S. Buchsbaum, M.D

This is a selective review of the work of Buchsbaum and colleagues. It revisits and pays tribute to four decades of publications employing positron emission tomography (PET) with ^F-18fluorodeoxyglucose (FDG) to examine the neurobiology of schizophrenia-spectrum disorders (including schizotypal personality disorder (SPD) and schizophrenia). Beginning with a landmark FDG-PET study in 1982 reporting hypofrontality in unmedicated schizophrenia patients, Buchsbaum and colleagues published high-impact work on regional glucose metabolic rate (GMR) abnormalities in the spectrum. Several key discoveries were made, including the delineation of schizophrenia-spectrum abnormalities in frontal and temporal lobe, cingulate, thalamus, and striatal regions using three-dimensional mapping with coregistered MRI and PET. These findings indicated that SPD patients have less marked frontal lobe and striatal dysfunction compared with schizophrenia patients, possibly mitigating frank psychosis. Additionally, these investigations were among the first to conduct early seed-based functional connectivity analyses with FDG-PET, showing aberrant cortical-subcortical circuitry and, in particular, revealing a thalamocortical circuitry abnormality in schizophrenia. Finally, pioneering work employing the first double-blind randomized antipsychotic (haloperidol) vs. placebo FDG-PET study design in schizophrenia indicated that GMR in the striatum, more than in any other region, was related to clinical response.

Neuroimaging in Psychiatric Disorders: A Bibliometric Analysis of the 100 Most Highly Cited Articles

BACKGROUND AND PURPOSE

Extensive research has been conducted to find neuroimaging biomarkers for psychiatric disorders. This study aimed at identifying trends of the 100 most highly cited articles on neuroimaging in primary psychiatric disorders.

METHODS

The most highly cited original research articles were identified and analyzed, following searches of MEDLINE and Web of Science All Databases.

RESULTS

The top 100 articles ranked by yearly citation (from 137.5 to 31.1) were published between 1989 and 2017. Depressive disorders (30 articles), schizophrenia spectrum and other psychotic disorders (27), autism spectrum disorder (17), substance-related and addictive disorders (7), and post-traumatic stress disorder (7) were among the most studied conditions. Functional magnetic resonance imaging (42), structural magnetic resonance imaging (30), and positron emission tomography (22) were the most utilized neuroimaging modalities. While 85 articles investigated the pathophysiology of psychiatric disorders (including 7 focusing on developmental changes and 1 on genetic susceptibility), 15 articles studied the impact of treatment, including antidepressants (6), deep brain stimulation (4), antipsychotics (3), behavior therapy (3), and exercise (1). The analysis also identified the most contributing authors, countries (the United States: 71 articles, the United Kingdom: 8, Canada: 5, and China: 5), and journals (JAMA Psychiatry: 20 articles and Biological Psychiatry: 17). Ninety-eight studies were prospective, and two were retrospective. The sample size ranged from 3 to 1,188 (median: 21).

CONCLUSIONS

Our study identified intellectual milestones in the utility of neuroimaging in investigating primary psychiatric disorders. The historic trends could help guide future research in this field.

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.

Impact of Global Mean Normalization on Regional Glucose Metabolism in the Human Brain

Because the human brain consumes a disproportionate fraction of the resting body's energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMR_glc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMR_glc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMR_glc alterations compared to control were detected in all conditions except in congenitally blind where regional CMR_glc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMR_glc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMR_glc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMR_glc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMR_glc data in health and disease.

Subjective Experience, Heterophenomenology, or Neuroimaging? A Perspective on the Meaning and Application of Mental Disorder Terms, in Particular Major Depressive Disorder

Increasing research efforts try to identify biological markers in order to support or eventually replace current practices of diagnosing mental disorders. Inasmuch as these disorders refer to subjective mental states, such efforts amount to their objectification. This gives rise to conceptual as well as empirical challenges: What kind of things are mental disorders? And how to deal with situations where subjective reports, clinical decisions, and brain scans contradict each other? The present paper starts out with a discussion of recent efforts to objectify beauty. Such attempts to quantify and localize psychological constructs in the brain are compared to earlier examples from the history of psychology. The paper then discusses personal and social implications of the objectification of subjective mental states, including mental disorders. The construct of Major Depressive Disorder, one of the most prevalent mental disorders, is then analyzed in more detail. It turns out that this is a very complex construct probably associated with highly heterogeneous actual instances of the disorder. It is then shown that it is unlikely to replace these symptoms’ descriptions with patterns of brain activations, at least in the near future, given these patterns’ empirical lack of specificity. The paper then discusses which of the disorder’s core symptoms are more or less amenable to behavioral or neuroscientific investigation and analyses whether the heterophenomenological method can solve the problem. The conclusion is that the disorder construct is neither entirely subjective, nor completely objectifiable, and that clinical experts do well by continuing to take a pragmatical stance.

How to Characterize the Function of a Brain Region

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

Non-invasive imaging modalities to study neurodegenerative diseases of aging brain

The aim of this article is to highlight current approaches for imaging elderly brain, indispensable for cognitive neuroscience research with emphasis on the basic physical principles of various non-invasive neuroimaging techniques. The first part of this article presents a quick overview of the primary non-invasive neuroimaging modalities used by cognitive neuroscientists such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), electroencephalography (EEG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance spectroscopic imaging (MRSI), Profusion imaging, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS) and diffusion tensor imaging (DTI) along with tractography and connectomics. The second part provides a comprehensive overview of different multimodality imaging techniques for various cognitive neuroscience studies of aging brain.

The brain's spontaneous activity and its psychopathological symptoms - "Spatiotemporal binding and integration"

Neuroimaging provided much insight into the neural activity of the brain and its alterations in psychiatric disorders. However, despite extensive research, the exact neuronal mechanisms leading to the various psychopathological symptoms remain unclear, yet. In addition to task-evoked activity during affective, cognitive, or other challenges, the brain's spontaneous or resting state activity has come increasingly into the focus. Basically all psychiatric disorders show abnormal resting state activity with the relation to psychopathological symptoms remaining unclear though. I here suggest to conceive the brain's spontaneous activity in spatiotemporal terms that is, by various mechanisms that are based on its spatial, i.e., functional connectivity, and temporal, i.e., fluctuations in different frequencies, features. I here point out two such spatiotemporal mechanisms, i.e., "spatiotemporal binding and integration". Alterations in the resting state's spatial and temporal features lead to abnormal "spatiotemporal binding and integration" which results in abnormal contents in cognition as in the various psychopathological symptoms. This, together with concrete empirical evidence, is demonstrated in depression and schizophrenia. I therefore conclude that we need to develop a spatiotemporal approach to psychopathology, "spatiotemporal psychopathology:" as I call it.

Resting-state functional magnetic resonance imaging for surgical planning in pediatric patients: a preliminary experience

OBJECTIVE Cerebral mapping for surgical planning and operative guidance is a challenging task in neurosurgery. Pediatric patients are often poor candidates for many modern mapping techniques because of inability to cooperate due to their immature age, cognitive deficits, or other factors. Resting-state functional MRI (rs-fMRI) is uniquely suited to benefit pediatric patients because it is inherently noninvasive and does not require task performance or significant cooperation. Recent advances in the field have made mapping cerebral networks possible on an individual basis for use in clinical decision making. The authors present their initial experience translating rs-fMRI into clinical practice for surgical planning in pediatric patients. METHODS The authors retrospectively reviewed cases in which the rs-fMRI analysis technique was used prior to craniotomy in pediatric patients undergoing surgery in their institution. Resting-state analysis was performed using a previously trained machine-learning algorithm for identification of resting-state networks on an individual basis. Network maps were uploaded to the clinical imaging and surgical navigation systems. Patient demographic and clinical characteristics, including need for sedation during imaging and use of task-based fMRI, were also recorded. RESULTS Twenty patients underwent rs-fMRI prior to craniotomy between December 2013 and June 2016. Their ages ranged from 1.9 to 18.4 years, and 12 were male. Five of the 20 patients also underwent task-based fMRI and one underwent awake craniotomy. Six patients required sedation to tolerate MRI acquisition, including resting-state sequences. Exemplar cases are presented including anatomical and resting-state functional imaging. CONCLUSIONS Resting-state fMRI is a rapidly advancing field of study allowing for whole brain analysis by a noninvasive modality. It is applicable to a wide range of patients and effective even under general anesthesia. The nature of resting-state analysis precludes any need for task cooperation. These features make rs-fMRI an ideal technology for cerebral mapping in pediatric neurosurgical patients. This review of the use of rs-fMRI mapping in an initial pediatric case series demonstrates the feasibility of utilizing this technique in pediatric neurosurgical patients. The preliminary experience presented here is a first step in translating this technique to a broader clinical practice.