The Center for Behavioral Neuroscience was launched in the fall of 1999 with support from the National Science Foundation, the Georgia Research Alliance, and our eight participating institutions (Georgia State University, Emory University, Georgia Institute of Technology, Morehouse School of Medicine, Clark-Atlanta University, Spelman College, Morehouse College, Morris Brown College). The CBN provides the resources to foster innovative research in behavioral neuroscience, with a specific focus on the neurobiology of social behavior. Center faculty working in collaboratories use diverse model systems from invertebrates to humans to investigate fear, aggression, affiliation, and reproductive behaviors. The addition of new research foci in reward and reinforcement, memory and cognition, and sex differences has expanded the potential for collaborations among Center investigators. Technology core laboratories develop the molecular, cellular, systems, behavioral, and imaging tools essential for investigating how the brain influences complex social behavior and, in turn, how social experience influences brain function.
In addition to scientific discovery, a major goal of the CBN is to train the next generation of behavioral neuroscientists and to increase the number of women and under-represented minorities in neuroscience. Educational programs are offered for K-12 students to spark an interest in science. Undergraduate and graduate initiatives encourage students to participate in interdisciplinary and inter-institutional programs, while postdoctoral programs provide a bridge between laboratories and allow the interdisciplinary research and educational ventures to flourish. Finally, the CBN is committed to knowledge transfer, partnering with community organizations to bring neuroscience to the public. This multifaceted approach through research, education, and knowledge transfer will have a major impact on how we study interactions between the brain and behavior, as well as how the public views brain function and neuroscience.
Waxy mutants, in which endosperm starch contains ∼100% amylopectin rather than
the wild-type composition of ∼70% amylopectin and ∼30% amylose,
occur in many domesticated cereals. The cultivation of waxy varieties is concentrated in
east Asia, where there is a culinary preference for glutinous-textured foods that may have
developed from ancient food processing traditions. The waxy phenotype results from
mutations in the GBSSI gene, which catalyzes amylose synthesis. Broomcorn
or proso millet (Panicum miliaceum L.) is one of the world’s oldest
cultivated cereals, which spread across Eurasia early in prehistory. Recent
phylogeographic analysis has shown strong genetic structuring that likely reflects ancient
expansion patterns. Broomcorn millet is highly unusual in being an allotetraploid cereal
with fully waxy varieties. Previous work characterized two homeologous
GBSSI loci, with multiple alleles at each, but could not determine
whether both loci contributed to GBSSI function. We first tested the relative contribution
of the two GBSSI loci to amylose synthesis and second tested the
association between GBSSI alleles and phylogeographic structure inferred
from simple sequence repeats (SSRs). We evaluated the phenotype of all known
GBSSI genotypes in broomcorn millet by assaying starch composition and
protein function. The results showed that the GBSSI-S locus is the major
locus controlling endosperm amylose content, and the GBSSI-L locus has
strongly reduced synthesis capacity. We genotyped 178 individuals from landraces from
across Eurasia for the 2 GBSSI and 16 SSR loci and analyzed
phylogeographic structuring and the geographic and phylogenetic distribution of
GBSSI alleles. We found that GBSSI alleles have
distinct spatial distributions and strong associations with particular genetic clusters
defined by SSRs. The combination of alleles that results in a partially waxy phenotype
does not exist in landrace populations. Our data suggest that broomcorn millet is a system
in the process of becoming diploidized for the GBSSI locus responsible
for grain amylose. Mutant alleles show some exchange between genetic groups, which was
favored by selection for the waxy phenotype in particular regions. Partially waxy
phenotypes were probably selected against—this unexpected finding shows that better
understanding is needed of the human biology of this phenomenon that distinguishes cereal
use in eastern and western cultures.
diploidization; domestication; granule bound starch synthase; millet; Panicum miliaceum; waxy starch
Management types and their intensity may vary according to indicators such as: (1) practices complexity, (2) degree of techniques specialization, (3) occurrence and types of social regulations, (4) artificial selection intensity, (5) energy invested, (6) tools types, and (7) amounts of resources obtained. Management types of edible plants were characterized and analyzed in Náhuatl communities of the Tehuacán Valley. We expected that both natural and human pressures generate risk on plant resources availability, influencing human responses of management directed to decrease risk. We particularly hypothesized that magnitude of risk would be a direct function of human pressures favored by cultural and economic value and ecological factors such as scarcity (restricted distribution and abundance). Management practices may decrease risk of plant resources, more effectively when they are more intense; however, absence or insufficiency of management practices on endangered plants may favor loss of their populations. Understanding current management motives and their consequences on the purpose of ensuring availability of plant resources might allow us to understand similar processes occurring in the past. This issue is particularly important to be studied in the Tehuacán Valley, where archaeologists documented possible scenarios motivating origins of plant management by agriculture during prehistory.
Through ethnobotanical collecting, 55 semi-structured and free listing interviews we inventoried edible plant species used in five villages of Coyomeapan, Mexico. We identified: (1) native plant species whose products are obtained exclusively through simple gathering, (2) native species involving simple gathering and other management types, and (3) non-native species managed by agricultural management. We conducted in depth studies on the 33 native species managed through gathering and other types of practices. We carried out a total of 660 sessions of detailed interviews to 20 households randomly selected. We showed to people voucher specimens and photos of the sample of species chosen and documented their cultural and economic values. Spatial availability of these plant species was evaluated through vegetation sampling. Values for each cultural, economic, and ecological indicator were codified and averaged or summed and weighed according to frequency of interviewees’ responses or ecological conditions per plant species. With the standardized values of these indicators we performed a PCA and scores of the first principal component were considered as a risk index, which summarizes information of thirteen indicators of human use, demand and scarcity of each plant species. Similarly, eleven indicators of energy invested, complexity, tools and management strategies were used for performing PCA and scores of the first principal component were considered as management intensity index for each plant species. A linear regression analysis was performed to analyze the relation between risk and management intensity indexes. Amounts of variation of management data explained by ecological, cultural and economic information, as well as their risk level were analyzed through canonical correspondence analyses (CCA).
A total of 122 edible plant species were recorded, nearly 30% of them were introduced domesticated plants, 51 were wild species obtained exclusively by simple gathering and 33 were native species obtained by simple gathering and other management practices, these latter were the ones more deeply studied. People recognized variants in 21 of these latter 33 species, the variants receiving differential use, management, artificial selection and incipient domestication. The lowest values of management intensity corresponded to species under simple gathering and tolerance, mostly annual abundant plants, occasionally consumed by few people. The highest management intensity values were recorded in species with economic importance, mostly perennial with recognized variants whose management requires using tools, and which are protected by collective regulations. The regression analysis indicated significant value R2 = 0.433 (P < 0.001) between risk and management indexes. CCA explained 65.5% of variation of management intensity, mainly by socio-cultural factors (32.6%), whereas ecological data explained 21.3% and the intersection of all factors 11.6%. Variation of management intensity is 67.6% explained by risk variables. Length-span of life cycle, reproductive system type, distribution, number of parts used, number of management and use forms and type of regulations were statistically significant.
People manage plant resources according to the role these play in households’ subsistence, the quantity available and the quality of their useful products; particularly important is the balance between resources availability and demand. Management responses to risk are also influenced by the ease to propagate or manipulate individual plants and time requiring the construction of manipulation strategies and techniques.
Domestication; Food Security; Plant Management; Risk Management; Tehuacán Valley
This paper reviews ethnographies of neuroscience laboratories in the United States and Europe, organizing them into three main sections: (1) descriptions of the capabilities and limitations of technologies used in neuroimaging laboratories to map “activity” or “function” onto structural models of the brain; (2) discussions of the “distributed” or “extended” mind in neuroscience practice; and (3) the implications of neuroscience research and the power of brain images outside the laboratory. I will try to show the importance of ethnographic work in such settings, and place this body of ethnographic work within its historical framework—such ethnographies largely emerged within the Decade of the Brain, as announced by former President of the United States George H. W. Bush in 1990. The main argument is that neuroscience research and the context within which it is taking place has changed since the 1990’s—specifically with the launch of “big science” projects such as the Human Brain Project (HBP) in the European Union and the BRAIN initiative in the United States. There is an opportunity for more research into the institutional and politico-economic context within which neuroscience research is taking place, and for continued engagement between the social and biological sciences.
ethnography; neuroscience; science and technology studies; human brain project; BRAIN
Studies in the basic neurosciences are heavily reliant upon rat and mouse models. The brain is one of the most distinguishing features of the human species, but is enough being done to fully understand the evolution of the human brain and brain diversity in general? Without a clear understanding of the evolution of the nervous system we may be investing a great deal of effort into some limited specific animal models that may prove to be erroneous in terms of the overall usefulness in clinically applied research. Here we present an analysis that demonstrates that 75% of our research efforts are directed to the rat, mouse and human brain, or 0.0001% of the nervous systems on the planet. This extreme bias in research trends may provide a limited scope in the discovery of novel aspects of brain structure and function that would be of importance in understanding both the evolution of the human brain and in selecting appropriate animal models for use in clinically related research. We offer examples both from the historical and recent literature indicating the usefulness of comparative neurobiological investigation in elucidating both normal and abnormal structure and function of the brain.
animal models; vertebrate; invertebrate; central nervous system; evolution
An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.
ontologies; ontology reuse; neuroscience ontology; semantic search
A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are characteristic to neurons in both normal and diseased brain. Towards this, a great deal of effort has been placed on building high-resolution neuroanatomical maps1–3. With the expansion of molecular genetics and advances in light microscopy has come the ability to query not only neuronal morphologies, but also the molecular and cellular makeup of individual neurons and their associated networks4. Major advances in the ability to mark and manipulate neurons through transgenic and gene targeting technologies in the rodent now allow investigators to ‘program’ neuronal subsets at will5–6. Arguably, one of the most influential contributions to contemporary neuroscience has been the discovery and cloning of genes encoding fluorescent proteins (FPs) in marine invertebrates7–8, alongside their subsequent engineering to yield an ever-expanding toolbox of vital reporters9. Exploiting cell type-specific promoter activity to drive targeted FP expression in discrete neuronal populations now affords neuroanatomical investigation with genetic precision.
Engineering FP expression in neurons has vastly improved our understanding of brain structure and function. However, imaging individual neurons and their associated networks in deep brain tissues, or in three dimensions, has remained a challenge. Due to high lipid content, nervous tissue is rather opaque and exhibits auto fluorescence. These inherent biophysical properties make it difficult to visualize and image fluorescently labelled neurons at high resolution using standard epifluorescent or confocal microscopy beyond depths of tens of microns. To circumvent this challenge investigators often employ serial thin-section imaging and reconstruction methods10, or 2-photon laser scanning microscopy11. Current drawbacks to these approaches are the associated labor-intensive tissue preparation, or cost-prohibitive instrumentation respectively.
Here, we present a relatively rapid and simple method to visualize fluorescently labelled cells in fixed semi-thick mouse brain slices by optical clearing and imaging. In the attached protocol we describe the methods of: 1) fixing brain tissue in situ via intracardial perfusion, 2) dissection and removal of whole brain, 3) stationary brain embedding in agarose, 4) precision semi-thick slice preparation using new vibratome instrumentation, 5) clearing brain tissue through a glycerol gradient, and 6) mounting on glass slides for light microscopy and z-stack reconstruction (Figure 1).
For preparing brain slices we implemented a relatively new piece of instrumentation called the ‘Compresstome’ VF-200 (http://www.precisionary.com/products_vf200.html). This instrument is a semi-automated microtome equipped with a motorized advance and blade vibration system with features similar in function to other vibratomes. Unlike other vibratomes, the tissue to be sliced is mounted in an agarose plug within a stainless steel cylinder. The tissue is extruded at desired thicknesses from the cylinder, and cut by the forward advancing vibrating blade. The agarose plug/cylinder system allows for reproducible tissue mounting, alignment, and precision cutting. In our hands, the ‘Compresstome’ yields high quality tissue slices for electrophysiology, immunohistochemistry, and direct fixed-tissue mounting and imaging. Combined with optical clearing, here we demonstrate the preparation of semi-thick fixed brain slices for high-resolution fluorescent imaging.
Neuroscience; Issue 53; nervous tissue; neurons; confocal; epifluorescent; imaging; clearing; fluorescent proteins
Among the multiple conservative modalities, physiotherapy is a commonly utilized treatment modality in managing chronic non-specific spinal pain. Despite the scientific progresses with regard to pain and motor control neuroscience, treatment of chronic spinal pain (CSP) often tends to stick to a peripheral biomechanical model, without targeting brain mechanisms. With a view to enhance clinical efficacy of existing physiotherapeutic treatments for CSP, the development of clinical strategies targeted at ‘training the brain’ is to be pursued. Promising proof-of-principle results have been reported for the effectiveness of a modern neuroscience approach to CSP when compared to usual care, but confirmation is required in a larger, multi-center trial with appropriate evidence-based control intervention and long-term follow-up.
The aim of this study is to assess the effectiveness of a modern neuroscience approach, compared to usual care evidence-based physiotherapy, for reducing pain and improving functioning in patients with CSP. A secondary objective entails examining the effectiveness of the modern neuroscience approach versus usual care physiotherapy for normalizing brain gray matter in patients with CSP.
The study is a multi-center, triple-blind, two-arm (1:1) randomized clinical trial with 1-year follow-up. 120 CSP patients will be randomly allocated to either the experimental (receiving pain neuroscience education followed by cognition-targeted motor control training) or the control group (receiving usual care physiotherapy), each comprising of 3 months treatment. The main outcome measures are pain (including symptoms and indices of central sensitization) and self-reported disability. Secondary outcome measures include brain gray matter structure, motor control, muscle properties, and psychosocial correlates. Clinical assessment and brain imaging will be performed at baseline, post-treatment and at 1-year follow-up. Web-based questionnaires will be completed at baseline, after the first 3 treatment sessions, post-treatment, and at 6 and 12-months follow-up.
Findings may provide empirical evidence on: (1) the effectiveness of a modern neuroscience approach to CSP for reducing pain and improving functioning, (2) the effectiveness of a modern neuroscience approach for normalizing brain gray matter in CSP patients, and (3) factors associated with therapy success. Hence, this trial might contribute towards refining guidelines for good clinical practice and might be used as a basis for health authorities’ recommendations.
ClinicalTrials.gov Identifier: NCT02098005.
Chronic pain; Low back pain; Neck pain; Education; Exercise; Motor control; Neuroscience; Randomized controlled trial
Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.
The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a ‘language of translation’ affords the opportunity to improve the objectivity of decision-making.
This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development.
The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification.
Functional Imaging; Translational; Drug Development; Brain; Drugs; Brain Disease; Animal Models; Translational Medicine
The rapidly growing field of cognitive neuroscience holds the promise of explaining the operations of the mind in terms of the physical operations of the brain. Some suggest that our emerging understanding of the physical causes of human (mis)behaviour will have a transformative effect on the law. Others argue that new neuroscience will provide only new details and that existing legal doctrine can accommodate whatever new information neuroscience will provide. We argue that neuroscience will probably have a transformative effect on the law, despite the fact that existing legal doctrine can, in principle, accommodate whatever neuroscience will tell us. New neuroscience will change the law, not by undermining its current assumptions, but by transforming people's moral intuitions about free will and responsibility. This change in moral outlook will result not from the discovery of crucial new facts or clever new arguments, but from a new appreciation of old arguments, bolstered by vivid new illustrations provided by cognitive neuroscience. We foresee, and recommend, a shift away from punishment aimed at retribution in favour of a more progressive, consequentialist approach to the criminal law.
Finding biomarkers of human neurological diseases is one of the most pressing goals of modern medicine. Most neurological disorders are recognized too late because of the lack of biomarkers that can identify early pathological processes in the living brain. Late diagnosis leads to late therapy and poor prognosis. Therefore, during the past decade, a major endeavor of clinical investigations in neurology has been the search for diagnostic and prognostic biomarkers of brain disease. Recently, a new field of metabolomics has emerged, aiming to investigate metabolites within the cell/tissue/organism as possible biomarkers. Similarly to other “omics” fields, metabolomics offers substantial information about the status of the organism at a given time point. However, metabolomics also provides functional insight into the biochemical status of a tissue, which results from the environmental effects on its genome background. Recently, we have adopted metabolomics techniques to develop an approach that combines both in vitro analysis of cellular samples and in vivo analysis of the mammalian brain. Using proton magnetic resonance spectroscopy, we have discovered a metabolic biomarker of neural stem/progenitor cells (NPCs) that allows the analysis of these cells in the live human brain. We have developed signal-processing algorithms that can detect metabolites present at very low concentration in the live human brain and can indicate possible pathways impaired in specific diseases. Herein, we present our strategy for both cellular and systems metabolomics, based on an integrative processing of the spectroscopy data that uses analytical tools from both metabolomic and spectroscopy fields. As an example of biomarker discovery using our approach, we present new data and discuss our previous findings on the NPC biomarker. Our studies link systems and cellular neuroscience through the functions of specific metabolites. Therefore, they provide a functional insight into the brain, which might eventually lead to discoveries of clinically useful biomarkers of the disease.
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.
The human brain is made up of billions of neurons, which are organised into networks via trillions of connections. The study of the nature of these connections will be central to understanding how the brain works. In recent years, a number of new methods for imaging the brain have made it possible to visualise and map these connections, generating striking images and creating an additional field of neuroscience known as ‘connectomics’.
However, the sheer volume of data generated by connectomics is now beginning to exceed the capacity of researchers to analyse it. Just as the advent of genome sequencing required the development of statistical techniques to analyse the resulting data, so the emergence of connectomics has created a need for similarly powerful mathematical models in neuroscience.
Jonas and Kording have developed one such algorithm that can classify the component units of circuits, both biological and man-made, and identify the connections between them. When applied to connectomics data for 950 neurons in the mouse retina, the algorithm generated predictions regarding cell types and patterns of connectivity. The predicted cell types agreed closely with those identified by human neuroanatomists. Results were similarly convincing when the algorithm was applied to the nervous system of the nematode worm and genetic model organism, Caenorhabditis elegans, and even when it was asked to classify electronic components and connectivity patterns in a man-made microprocessor.
Algorithms such as that developed by Jonas and Kording will soon be essential for making sense of the vast quantities of data generated by connectomic studies of the human brain. At present, an analysis of 950 neurons requires several hours, thus refinements that make the process faster will likely be required prior to the analysis of larger human datasets. Such algorithms will open up a range of possibilities for examining the structure of the healthy brain, as well as the changes triggered by developmental abnormalities and disease.
connectomics; computation; microcircuitry; C. elegans; mouse
Measurements of human brain function in children are of increasing interest in cognitive neuroscience. Many techniques for brain mapping used in children, including functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS), use probes placed on or near the scalp. The distance between the scalp and the brain is a key variable for these techniques because optical, electrical and magnetic signals are attenuated by distance. However, little is known about how scalp-brain distance differs between different cortical regions in children or how it changes with development. We investigated scalp-brain distance in 71 children, from newborn to age 12 years, using structural T1-weighted MRI scans of the whole head. Three-dimensional reconstructions were created from the scalp surface to allow for accurate calculation of brain-scalp distance. Nine brain landmarks in different cortical regions were manually selected in each subject based on the published fNIRS literature. Significant effects were found for age, cortical region and hemisphere. Brain-scalp distances were lowest in young children, and increased with age to up to double the newborn distance. There were also dramatic differences between brain regions, with up to 50% differences between landmarks. In frontal and temporal regions, scalp-brain distances were significantly greater in the right hemisphere than in the left hemisphere. The largest contributors to developmental changes in brain-scalp distance were increases in the corticospinal fluid (CSF) and inner table of the cranium. These results have important implications for functional imaging studies of children: age and brain-region related differences in fNIRS signals could be due to the confounding factor of brain-scalp distance and not true differences in brain activity.
The investigation of cultural phenomena using neuroscientific methods—cultural neuroscience (CN)—is receiving increasing attention. Yet it is unclear whether the integration of cultural study and neuroscience is merely additive, providing additional evidence of neural plasticity in the human brain, or truly synergistic, yielding discoveries that neither discipline could have achieved alone. We discuss how the parent fields to CN: cross-cultural psychology, psychological anthropology and cognitive neuroscience inform the investigation of the role of cultural experience in shaping the brain. Drawing on well-established methodologies from cross-cultural psychology and cognitive neuroscience, we outline a set of guidelines for CN, evaluate 17 CN studies in terms of these guidelines, and provide a summary table of our results. We conclude that the combination of culture and neuroscience is both additive and synergistic; while some CN methodologies and findings will represent the direct union of information from parent fields, CN studies employing the methodological rigor required by this logistically challenging new field have the potential to transform existing methodologies and produce unique findings.
cross-cultural; cross disciplinary; cultural-neuroscience; culture; neuroscience; neuroimaging
Autism spectrum disorders (ASD) represent a formidable challenge for
psychiatry and neuroscience because of their high prevalence, life-long nature,
complexity and substantial heterogeneity. Facing these obstacles requires
large-scale multidisciplinary efforts. While the field of genetics has pioneered
data sharing for these reasons, neuroimaging had not kept pace. In response, we
introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots
consortium aggregating and openly sharing 1112 existing resting-state functional
magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI
and phenotypic information from 539 individuals with ASD and 573 age-matched
typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we
present this resource and demonstrate its suitability for advancing knowledge of
ASD neurobiology based on analyses of 360 males with ASD and 403 male
age-matched TC. We focused on whole-brain intrinsic functional connectivity and
also survey a range of voxel-wise measures of intrinsic functional brain
architecture. Whole-brain analyses reconciled seemingly disparate themes of both
hypo and hyperconnectivity in the ASD literature; both were detected, though
hypoconnectivity dominated, particularly for cortico-cortical and
interhemispheric functional connectivity. Exploratory analyses using an array of
regional metrics of intrinsic brain function converged on common loci of
dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and
highlighted less commonly explored regions such as thalamus. The survey of the
ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication
and novel discovery. By pooling multiple international datasets, ABIDE is
expected to accelerate the pace of discovery setting the stage for the next
generation of ASD studies.
Resting state fMRI; Intrinsic functional connectivity; Data sharing; Large-scale networks; Default network; Interhemispheric connectivity; Thalamus
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.
structural equation modeling; functional connectivity; structural connectivity; resting state; functional magnetic resonance imaging; tensor diffusion imaging
The human brain undergoes dramatic maturational changes during late stages of fetal and early postnatal life. The importance of this period to the establishment of healthy neural connectivity is apparent in the high incidence of neural injury in preterm infants, in whom untimely exposure to ex-uterine factors interrupts neural connectivity. Though the relevance of this period to human neuroscience is apparent, little is known about functional neural networks in human fetal life. Here, we apply graph theoretical analysis to examine human fetal brain connectivity. Utilizing resting state functional magnetic resonance imaging (fMRI) data from 33 healthy human fetuses, 19 to 39 weeks gestational age (GA), our analyses reveal that the human fetal brain has modular organization and modules overlap functional systems observed postnatally. Age-related differences between younger (GA <31 weeks) and older (GA≥31 weeks) fetuses demonstrate that brain modularity decreases, and connectivity of the posterior cingulate to other brain networks becomes more negative, with advancing GA. By mimicking functional principles observed postnatally, these results support early emerging capacity for information processing in the human fetal brain. Current technical limitations, as well as the potential for fetal fMRI to one day produce major discoveries about fetal origins or antecedents of neural injury or disease are discussed.
Optical brain imaging has seen 30 years of intense development, and has grown into a rich and diverse field. In-vivo imaging using light provides unprecedented sensitivity to functional changes through intrinsic contrast, and is rapidly exploiting the growing availability of exogenous optical contrast agents. Light can be used to image microscopic structure and function in vivo in exposed animal brain, while also allowing noninvasive imaging of hemodynamics and metabolism in a clinical setting. This work presents an overview of the wide range of approaches currently being applied to in-vivo optical brain imaging, from animal to man. Techniques include multispectral optical imaging, voltage sensitive dye imaging and speckle-flow imaging of exposed cortex, in-vivo two-photon microscopy of the living brain, and the broad range of noninvasive topography and tomography approaches to near-infrared imaging of the human brain. The basic principles of each technique are described, followed by examples of current applications to cutting-edge neuroscience research. In summary, it is shown that optical brain imaging continues to grow and evolve, embracing new technologies and advancing to address ever more complex and important neuroscience questions.
optical imaging; two-photon microscopy; near-infrared spectroscopy; diffuse optical tomography; neuroimaging; neurovascular coupling
Elucidating the brain basis for psychological processes and behavior is a fundamental aim of cognitive neuroscience. The lesion method, using voxel-based statistical analysis, is an important approach to this goal, identifying neural structures that are necessary for the support of specific mental operations, and complementing the strengths of functional imaging techniques.
Lesion coverage in a population is by nature spatially heterogeneous and biased, systematically affecting the ability of lesion–deficit correlation methods to detect and localize functional associations. We have developed a simulator that allows investigators to model parameters in a lesion–deficit study and characterize the statistical bias in lesion deficit detection coverage that will result from specific assumptions. We used the simulator to assess the signal detection properties and localization accuracy of standard lesion–deficit correlation methods, under a simple truth model — that a critical region of interest (CR), when damaged, gives rise to a deficit. We considered voxel-based lesion-symptom mapping (VLSM) and proportional MAP-3 (PM3). Using regression analysis, we examined if the pattern of outcome statistics can be explained by simulation parameters, factors that are inherent to anatomic parcels, and lesion coverage of the population, which consisted of a representative sample of 351 subjects drawn from the Iowa Patient Registry. We examined the effect of using nonparametric versus parametric statistics to obtain thresholded maps and the effect of correcting for multiple comparisons using false discovery rate or cluster-based correction.
Our results, which are derived from samples of realistic lesions, indicate that even a simple truth model yields localization errors that are systematic and pervasive, averaging 2 cm in the standard anatomic space, and tending to be directed towards areas of greater anatomic coverage. This displacement positions the center of mass of the detected region in a different anatomical region 87% of the time. This basic result is not affected by the choice of PM3 vs VLSM as the fundamental approach, nor is localization error ameliorated by incorporation of lesion size as a covariate in the VLSM approach, or by data distribution-driven approaches to controlling multiple spatial comparisons (false discovery rate or cluster-based correction approaches).
Our simulations offer a quantitative basis for interpreting lesion studies in cognitive neuroscience. We suggest ways in which lesion simulation and analysis frameworks could be productively extended.
•We assessed the signal detection properties and localization accuracy of lesion–deficit correlation methods•Localization errors are pervasive regardless of statistical method or whether controlling for multiple comparisons•The power of lesion deficit analysis is limited by the number of subjects with a lesion and a deficit•The detected center of mass tends to be skewed towards regions of higher coverage, and is a function of the spatial distribution of lesion coverage•The simulator approach offers a way to evaluate modifications to the lesion method to address weaknesses of the method
Lesion studies; VLSM; PM3; Lesion–deficit relationship
Neuropsychological research on the neural basis of behaviour generally posits that brain mechanisms will ultimately suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms relevant to neuroscience can therefore be formulated solely in terms of properties of these elements. Thus, terms having intrinsic mentalistic and/or experiential content (e.g. ‘feeling’, ‘knowing’ and ‘effort’) are not included as primary causal factors. This theoretical restriction is motivated primarily by ideas about the natural world that have been known to be fundamentally incorrect for more than three-quarters of a century. Contemporary basic physical theory differs profoundly from classic physics on the important matter of how the consciousness of human agents enters into the structure of empirical phenomena. The new principles contradict the older idea that local mechanical processes alone can account for the structure of all observed empirical data. Contemporary physical theory brings directly and irreducibly into the overall causal structure certain psychologically described choices made by human agents about how they will act. This key development in basic physical theory is applicable to neuroscience, and it provides neuroscientists and psychologists with an alternative conceptual framework for describing neural processes. Indeed, owing to certain structural features of ion channels critical to synaptic function, contemporary physical theory must in principle be used when analysing human brain dynamics. The new framework, unlike its classic-physics-based predecessor, is erected directly upon, and is compatible with, the prevailing principles of physics. It is able to represent more adequately than classic concepts the neuroplastic mechanisms relevant to the growing number of empirical studies of the capacity of directed attention and mental effort to systematically alter brain function.
mind; consciousness; brain; neuroscience; neuropsychology; quantum mechanics
Cultural neuroscience is set to flourish in the next few years. As the field develops, it is necessary to reflect on what is meant by ‘culture’ and how this can be translated for the laboratory context. This article uses the example of the adolescent brain to discuss three aspects of culture that may help us to shape and reframe questions, interpretations and applications in cultural neuroscience: cultural contingencies of categories, cultural differences in experience and cultural context of neuroscience research. The last few years have seen a sudden increase in the study of adolescence as a period of both structural and functional plasticity, with new brain-based explanations of teenage behaviour being taken up in education, policy and medicine. However, the concept of adolescence, as an object of behavioural science, took shape relatively recently, not much more than a hundred years ago and was shaped by a number of cultural and historical factors. Moreover, research in anthropology and cross-cultural psychology has shown that the experience of adolescence, as a period of the lifespan, is variable and contingent upon culture. The emerging field of cultural neuroscience has begun to tackle the question of cultural differences in social cognitive processing in adults. In this article, I explore what a cultural neuroscience can mean in the case of adolescence. I consider how to integrate perspectives from social neuroscience and anthropology to conceptualize, and to empirically study, adolescence as a culturally variable phenomenon, which, itself, has been culturally constructed.
adolescence; culture; context; brain development; neuroscience; anthropology
Progranulin (PGRN) is a growth modulating factor released by a variety of cells. This molecule has gained the attention of the neuroscience community with recent discoveries of multifunctional roles of PGRN in normal brain and neurodegenerative disorders. We focus on novel roles of PGRN as a sex steroid-responsible gene in the developing and adult rodent brain. While the developing brain is feminine by default, hormone exposure, including androgen and estrogen, induces musculinization during the critical period. We have shown that PGRN is a sex steroid-responsible gene that may be involved in masculinization of the perinatal rat brain. We also found that in adult rats PGRN gene expression was up-regulated by estrogen in the hippocampus, suggesting that PGRN may mediate the mitogenic effects of estrogen in the active area of neurogenesis. Since it has been recently reported that mutations in PGRN gene are responsible for a type of frontotemporal lobar degeneration in humans, PGRN appears to be also involved in modulating neurodegeneration. Together, PGRN gene expression is induced by estrogen in both developing and adult brains, and it may play multifunctional roles in the organization of functional masculinization in the developing brain and the maintenance of adult brain function.
brain; estrogen; neurogenesis; progranulin; sexual differentiation
Great European mountain ranges have acted as barriers to gene flow for resident populations since prehistory and have offered a place for the settlement of small, and sometimes culturally diverse, communities. Therefore, the human groups that have settled in these areas are worth exploring as an important potential source of diversity in the genetic structure of European populations. In this study, we present new high resolution data concerning Y chromosomal variation in three distinct Alpine ethno-linguistic groups, Italian, Ladin and German. Combining unpublished and literature data on Y chromosome and mitochondrial variation, we were able to detect different genetic patterns. In fact, within and among population diversity values observed vary across linguistic groups, with German and Italian speakers at the two extremes, and seem to reflect their different demographic histories. Using simulations we inferred that the joint effect of continued genetic isolation and reduced founding group size may explain the apportionment of genetic diversity observed in all groups. Extending the analysis to other continental populations, we observed that the genetic differentiation of Ladins and German speakers from Europeans is comparable or even greater to that observed for well known outliers like Sardinian and Basques. Finally, we found that in south Tyroleans, the social practice of Geschlossener Hof, a hereditary norm which might have favored male dispersal, coincides with a significant intra-group diversity for mtDNA but not for Y chromosome, a genetic pattern which is opposite to those expected among patrilocal populations. Together with previous evidence regarding the possible effects of “local ethnicity” on the genetic structure of German speakers that have settled in the eastern Italian Alps, this finding suggests that taking socio-cultural factors into account together with geographical variables and linguistic diversity may help unveil some yet to be understood aspects of the genetic structure of European populations.
Linguistic evolution mirrors cultural evolution, of which one of the most decisive steps was the "agricultural revolution" that occurred 11,000 years ago in W. Asia. Traditional comparative historical linguistics becomes inaccurate for time depths greater than, say, 10 kyr. Therefore it is difficult to determine whether decisive events in human prehistory have had an observable impact on human language. Here we supplement the traditional methodology with independent statistical measures showing that following the transition to agriculture, languages of W. Asia underwent a transition from biconsonantal (2c) to triconsonantal (3c) morphology. Two independent proofs for this are provided. Firstly the reconstructed Proto-Semitic fire and hunting lexicons are predominantly 2c, whereas the farming lexicon is almost exclusively 3c in structure. Secondly, while Biblical verbs show the usual Zipf exponent of about 1, their 2c subset exhibits a larger exponent. After the 2c > 3c transition, this could arise from a faster decay in the frequency of use of the less common 2c verbs. Using an established frequency-dependent word replacement rate, we calculate that the observed increase in the Zipf exponent has occurred over the 7,500 years predating Biblical Hebrew namely, starting with the transition to agriculture.