Method of Multiple Working Hypotheses [jnl article]

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Then this morphological classification was further elaborated in association with cortical layers and cell function Meynert, This revealed the complex relationship between nerve cell morphology and function. However, were these morphological descriptions a reliable way to classify neurons? Confronted with the subjectivity of these morphological classifications determined by single investigators, some researchers tried to establish objective criteria to classify nerve cells by their electrophysiological or biochemical features.

Nissl , using basic aniline dyes, classified nerve cells according to which parts of the cell content were stained and which parts were not and the relationships between the stained and unstained parts. Neurons were also classified by the velocity of their action potentials measured with the cathode ray oscilloscope Gasser and Erlanger, Due to a better understanding of the chemical transmission of nerve impulses, neurons were divided into two types: cholinergic and adrenergic cells Dale, However, electrophysiological and biochemical states are limited by their sensitive condition-dependence.

Faced with this problem, researchers attempted to characterize neurons with more stable features. High-throughput, multiplexed methods, such as multiplexed fluorescence in situ hybridization FISH and in situ sequencing methods, are being developed to scale up the enterprise of neuronal cell-type classification Ke et al.

However, a comprehensive census of neuronal cell types is still out of reach. What are the major challenges? Neuroscience aims to achieve a comprehensive census of neurons and glial cells in the brain, with molecular annotation at subcellular resolution, such as mRNA expression, ion channels and synaptic proteins. Due to these factors, all neuronal classifications are provisional and hypothetical. It is true that every neuron appears unique, but we have to reduce dimensionality by defining a relevant level of granularity to identify neuronal types.

It is true that gene expression in cells is dynamic, but we have to find out their molecular ground states that maintain cell identity. So, the question is: how can we overcome the barriers of scale and complexity to achieve a reliable neuronal cell-type classification? Arising from a stem, dispersed into leaves spreading out in a circular shape to form cavities, in the eyes of a 17th-century anatomist, the extending nerve tracts in the brain formed loose nets and ventricles like the leaves of a cabbage Malpighi and Fracassati, Since Ancient Greece, nerve tracts had been considered related to brain function Tannery, A question then arose: how to trace these tracts?

About years ago, white matter was observed to be composed of fibrils arranged in bundles through the scraping method of dissection Vieussens, The first category connected the two hemispheres, including the corpus callosum, the corpora quadrigemina, the anterior and posterior commissures, the cerebral peduncles, the pons, the anterior medullary velum, the interthalamic adhesion and the trigeminal tubercle. The second category was supposed to assure the communication between the base and other parts of the brain, including the arcuate fasciculus, the pillars of the fornix, the peduncles of the pineal gland, the tracts connecting the mammillary tubercles and the anterior thalamic tubercles.

More than 20 years later, the projection system was identified through blunt dissection, including afferent and efferent fiber pathways linking the cortex with the subcortical regions, the brain stem and the spinal cord Gall and Spurzheim, However, dissection techniques could not determine the precise trajectory and arrangement of nerve tracts. Detailed tract tracing only became possible with the development of histological methods.

Using a Zeiss-microscope and carmine or gold chloride staining, Theodor Meynert identified clearly the three main types of white matter tracts: the association systems—the short arcuate fibers and long association fibers connecting the various parts of the cerebral cortex; the commissural pathways connecting the two hemispheres; the afferent and efferent projection systems linking the cortex to the subcortical structures Meynert, Early tracing studies, relying on physical diffusion of dyes in fixed material, were limited to large fiber tracts between brain regions.

The studies of neurocircuitry required more refined methods applicable to living tissue. However, lesions were usually nonspecific, degeneration altered the normal morphology of neurons, and pathological changes were extremely variable Cowan et al. To remedy this, tracing methods exploiting axonal transport in living neurons were developed in the s. Retrograde tracing techniques introduced an enzyme or fluorescent tracer in a downstream location relative to the targeted neurons, capable of labeling the somas of the neurons projecting to the injection site, but unable to visualize the fiber pathways linking them Kristensson, ; Kristensson and Olsson, ; LaVail and LaVail, This problem was resolved by anterograde tracing techniques, based on macromolecule transport from the soma to the axon terminals, such as autoradiographic tracing method Cowan et al.

Nevertheless, injections of tracers usually resulted in indiscriminate labeling of different types of neurons, and the surgical procedure to introduce an exogenous tracer was complex. To deal with this, tracing techniques exploiting genetic engineering were developed more than 20 years ago Prasher et al. These techniques were even adapted for live imaging of intact animals such as Drosophila Boulina et al.

The leaves of a cabbage have become a forest of rainbow trees. However, these tracing methods are limited to anatomical connectivity, which alone is not sufficient to account for brain function, because the synapse is dynamic Tsodyks and Markram, Therefore, physiological methods were invented. Owing to intracellular recording techniques, synaptic plasticity was better understood, such as the quantal release of neurotransmitters Fatt and Katz, , central synaptic inhibition Coombs et al.

Neural plasticity also inspired theoretical studies, such as Hebbian cell assembly and learning rule Hebb, and the theoretical study of STDP Abbott and Blum, ; Gerstner et al. Theoretical approach abstracts away detailed biological mechanisms to loosely model neural connectivity by building artificial neural networks. About 75 years ago, the first mathematical model of a simplified neural network appeared McCulloch and Pitts, , which led to the computational theory of mind and machine learning. However, to get deep insights into the detailed neural structures and mechanisms underlying brain function, we still need biologically realistic models.

Although the aforementioned experimental methods are able to trace neuronal connections on the cellular or even molecular scale, these invasive techniques are limited to postmortem brain tissue and experimental animals. To better understand our own brain, would it be possible to trace the neural connections in the living human brain?

In the early s, the development of noninvasive neuroimaging techniques, in particular MRI, made it possible to study the structural and functional connectivity of the human brain in vivo Damadian, ; Lauterbur, Nevertheless, generally, MRI methods can only trace neural connections between brain regions usually with millimeter resolution. Over the past years, connectivity mapping has evolved from gross tracing of major tracts in fixed brains to mapping neuronal projections with cellular and molecular resolution in living tissue, from mapping static neural connectivity to dynamic synaptic plasticity, from postmortem studies to in vivo large-scale mapping of human brain connectivity including structure, function and gene expression.

Science dreams of completeness. The concept of the connectome originated from the long-held belief that neural connections are related to brain functions, as illustrated by tract tracing since the 17th century. This relationship has been further revealed by recent research: at the microscale, synaptic connectivity is linked to neuronal network dynamics Chambers and MacLean, ; at the macroscale, the anatomical connectivity of the brain is related to its functional connectivity and different states Hermundstad et al.

Since the function of neural circuits and systems cannot be explained only through wiring diagrams, we also need information such as the types of neurons and synapses, the dynamics of neuronal synchronization, and the role of different types of glial cells and neuromodulators Sporns, b ; Fields et al. Nevertheless, this concept owes its origins to MRI methods, which enable in vivo rapid-throughput mapping of human brain connectivity at the macroscale. Macroconnectomics aims to map all the neural connections between gray matter regions at millimeter resolution.

It is best suited to in vivo human studies with neuroimaging methods, where few of fine-scale methods used in laboratory animals are applicable Sporns, b ; Van Essen, MRI, the major noninvasive neuroimaging technique for in vivo human connectome mapping, was developed in the early s, first used to diagnose cancer Damadian, ; Weisman et al. Invented in the s, dMRI uses water diffusion anisotropy along myelinated axons to map large white matter fiber bundles, combined with probabilistic tractography to estimate fiber trajectories Le Bihan and Breton, ; Margulies et al.

About 30 years ago, the first dMRI images of the human brain were obtained at 0. Since then, the sensitivity to diffusion has augmented about times McNab et al. To improve spatial resolution of white matter fiber tracking, ultrahigh field magnetic resonance engineering is a basic solution. MRI for clinical use is usually at 1. Human brain in vivo imaging was already performed at 9.

The final resolution also depends on the acquisition and reconstruction of diffusion images. For example, reconstructing nerve fiber orientations, especially in brain regions where fibers of multiple orientations intersect, involves a trade-off between the accuracy of the peak orientation and the sensitivity to crossing fibers and minor fiber bundles Van Essen et al. Hitherto, the highest resolution for the human brain achieved at 7T is 0. However, even this rare performance is not sufficient to study the connections between individual neurons.

Developed in the early s, fMRI first used contrast agents administrated intravenously Belliveau et al. Functional MRI includes two main methods: resting-state fMRI rsfMRI , measuring correlations in spontaneous activity between brain regions in resting subjects, and task-evoked fMRI tfMRI , trying to detect functionally distinct brain regions during various tasks such as visuomotor or cognitive processes Glasser et al.

Almost 30 years ago, human fMRI studies were mostly performed at 1. Since then, the spatial resolution of fMRI has been largely improved, such as the achievement of 0. Furthermore, the temporal resolution of fMRI is fundamentally limited by the nature of BOLD signals, which only indirectly reflect neuronal activity. Due to the temporal dynamics of neurovascular coupling, the peak of BOLD response to a neural stimulus occurs with 5—6 s delay Glover, Although MRI is a useful tool for studying human brain connectivity in vivo , it offers little data on the connectivity between neurocircuits and between individual neurons that is essential for understanding the mechanisms underlying brain function.

Hence the need for meso-, micro- and nano-connectomics. Meso- and micro-connectomics aim to map all the connections between different neuronal groups defined by cell types or connectivity patterns and between individual neurons at the micrometer scale. Such studies, using invasive techniques, are limited to experimental animals and postmortem human brain tissue. The first mesoconnectome, capturing cell type-specific connections as well as short- and long-range interregional axonal projections, was achieved in the mouse in , through enhanced green fluorescent protein EGFP -expressing adeno-associated viral vectors and high-throughput serial two-photon tomography Oh et al.

T. C. Chamberlin's "Method of Multiple Working Hypotheses": An encapsulation for modern students

However, dyes could only be applied to small blocks of tissue, making this method unsuitable for tracing long-distance connections. To resolve this problem, chemical markers were injected into circumscribed neural areas, which, however, could not label selectively different types of neurons Kristensson, ; Kristensson and Olsson, ; Cowan et al. More recently, non-optical, high-throughput methods were invented, such as Barcoding of Individual Neuronal Connections BOINC , which barcodes individual neurons and introduces transsynaptic viruses to map synaptic connections, based on high-throughput DNA sequencing Zador et al.

Nevertheless, due to several factors, connectivity reconstructed by this method is difficult to interpret as neuronal connectivity with single-synapse precision Oyibo et al. This barrier was finally broken by super-resolution microscopy developed in the late 20th century, which can routinely resolve a few tens of nanometers, such as stimulated emission-depletion STED microscope Hell and Wichmann, , structured illumination microscopy SIM; Gustafsson, and photoactivated localization microscopy PALM; Betzig et al.

Yet, even so, major challenges still lie ahead, in particular, mapping connections in small neural areas where many cells are targeted at the same time and where the connection density is high Lichtman et al. This may require a resolution of a few nanometers Huang et al. How to map neuronal connections at this scale?

Nanoconnectomics uses EM, the only method capable of identifying unequivocally synapses and gap junctions at nanometer or even sub-nanometer resolution. EM provides high-resolution validation of macro-, meso- and micro-connectomes. The first electron microscope, a transmission electron microscope TEM , was built in , only capable of Another major type of EM is scanning electron microscopy SEM , introduced in von Ardenne, , capable of sub-nanometer resolution Masters et al.

TEM remains to date the highest resolution technology able to validate specific gap junctions and small synapses requiring, for example, 0. However, EM methods are extremely time-consuming and labor-intensive, so currently limited to very small postmortem specimens. The first and the only almost complete nanoconnectome, that of Caenorhabditis elegans hermaphrodite, whose nervous system has in total neurons, was achieved in with serial-section TEM, containing about 5, chemical synapses, 2, neuromuscular junctions and gap junctions White et al.

Today, studies continue to fill the gaps in this original connectome and to address further questions such as the nature of individuality and how genetic and environmental factors regulate connectivity Mulcahy et al. The goal of connectomics is to experimentally map a full connectome of the mammalian brain, and ultimately the human brain.

Is this achievable? Although MRI methods are capable of large-scale, rapid-throughput mapping of human brain connectivity at macroscale, MRI-derived macroconnectomes result from data reduction, simplification and assumptions, and they do not necessarily reflect the actual structure and function of the brain.

MRI methods suffer from low spatial resolution. However, the basic mechanism underlying water diffusion in neural tissue, especially the role of cell membranes in modulating water diffusion, remains to be clarified, hence the fundamental limitation of the sensitivity of dMRI resides in the complexity of water diffusion in the microenvironment of the brain Van Essen et al.

Data processing introduces artifacts and distortions that are difficult to distinguish from actual neural connections Jones et al. The sensitivity of fMRI is affected by the fundamental problem of neurovascular coupling. BOLD signals reflect a complex combination of vascular system dynamics as well as the activity of neurons, astrocytes Iadecola and Nedergaard, , interneurons Cauli et al. However, the way all these elements contribute to fMRI signals still remains to be clarified.

Furthermore, fMRI detects only functional correlations between brain regions, and most functional connections show significant temporal fluctuations depending on measurement and analysis methods—they do not necessarily reflect the causal relationships between neural connections Friston, This means that the interpretation of results is often doubtful. From this point of view, current macroconnectome maps do not offer an actual image of the brain. Reproducibility is also a major concern for MRI studies Zuo et al. The storage and processing of gigantic volumes of data are problematic Schreiner et al.

The first fairly complete reconstruction of the C. Recent local circuit mapping by EM has high data output rates of gigabytes per minute Helmstaedter and Mitra, And this is just for the anatomical data, but what if we include the electrophysiological, biophysical and biochemical counterparts? Methods such as machine learning and crowdsourcing are gradually reducing the problem Kim et al.

Therefore, any connectome map represents only a snapshot of the dynamic brain; and 3 neurons can rapidly change their functional roles in response to chemical signals such as peptides, hormones or neuromodulators, all with no visible modification to the connectivity diagram, and each wiring diagram can encode many possible circuit outcomes Bargmann and Marder, However, if we want to understand the neural mechanisms underlying brain function, we have to identify their constituent neural connections from the molecular and cellular levels to the whole brain.

What is the link between verbal memory and bulging eyes, the cerebellum and sexuality? Gall noticed that individuals with a retentive verbal memory had bulging eyes and that several cases of aphasia were caused by the damage to the frontal lobe. Therefore, he localized verbal memory in the frontal lobes, assuming that the super development of these lobes pushed out the eyes. Feeling the burning nape of a nymphomaniac widow, he considered the cerebellum to be the organ of the sexual instinct Gall et al. Although phrenology was based on such false assumptions, it drove the functional mapping of the brain.

After all, Gall was not completely wrong with the relation between brain structure and function, which has been partly supported by some modern studies, in particular, the famous MRI study showing that London taxi drivers have larger posterior hippocampi Maguire et al. To surpass the simplistic correlation between cranial bumps and mental faculties, functional mapping further developed in cytoarchitectonics and myeloarchitectonics to build maps of cerebral regions according to their structure and inferred function. Motor function was one of the first functions to be located in the brain, owing to the identification of the giant pyramidal cells Meynert, ; Betz, ; Lewis, ; Campbell, Five years later, Brodmann distinguished 43 cytoarchitectonic areas in the human cortex, using cell body-stained histological sections, and assigned to each of them a function.

Methods in cytoarchitectonics and myeloarchitectonics mapped brain functions to brain areas mainly by inference. To relate directly behaviors to brain regions, clinicopathological correlation was one of the first methods developed in the history of functional mapping. The faculty of speech was located in the anterior lobes, the lesions to which led frequently to the loss of speech Bouillaud, ; Broca, These early studies suggested that the brain consisted of specific, circumscribed, yet interconnected functional areas, the disconnection of which caused neurological disorders.

This led to the concept of disconnection syndromes, caused by the destruction of either the centers of convergence where crucial associations were formed or the conduction pathways transmitting information between these centers Wernicke, ; Dejerine, The concept of disconnection syndromes was further developed in the s: the studies of split-brain patients revealed the topographic organization and functional specificity of the corpus callosum Gazzaniga et al.

The method of multiple working hypotheses | Climate Etc.

This was one strong argument held by holists. They considered that brain functions were distributed continuously throughout the brain: stimulation of a single point in the nervous system stimulated the whole system; a weakened point weakened the whole system Flourens, In the late 20th century, brain functions and dysfunctions were further investigated in vivo in human subjects with neuroimaging techniques, in particular positron emission tomography PET and fMRI Frackowiak, , Today, the relationship between segregation and integration, localized and distributed aspects of brain functions still poses a major challenge to neuroscience Cauda et al.

To directly test the function of brain regions, experimental methods, in particular, electrical stimulation and ablation techniques were developed. Through electrical stimulation that induced motor responses, the motor centers were first mapped in the dog cerebral cortex Hitzig and Fritsch, , then in a patient with a cranial malformation exposing parts of both cerebral hemispheres Bartholow, These results were demonstrated by destructing the motor centers in the monkey brain, which caused motor paralysis totally dissociated from sensory paralysis Ferrier, Owing to ablation techniques, vision was located in the occipital lobe and auditory function in the temporal lobe Panizza, ; Munk, And ablation of the frontal lobe in monkey was found to disintegrate the personality and to destroy the ability to classify and synthesize groups of representations Bianchi, However, these experimental methods suffered from low resolution and lacked specificity.

Nevertheless, both of these approaches could not resolve how different types of brain cells and circuits interact together to generate the full array of diverse brain functions. Functional mapping is evolving towards causally linking brain structure to function with high resolution and specificity. How does modern neuroscience face this major challenge? Current correlation-based methods are particularly represented by fMRI studies that detect the similarity of regional activation profiles reflected indirectly in BOLD signals to extract patterns of correlation or covariance and to infer functional connectivity between brain regions.

The biophysics of how BOLD signals relate to underlying neural activity remains an unsolved question and represents a fundamental limitation of fMRI studies Hillman, ; Gao et al. Since correlation-based methods deliver non-causal similarity-based metrics of statistical dependence Bassett and Sporns, , other methods are used to unravel the causal relationship between neural activity and brain function, in particular recording and manipulating neural activity and observing the behavioral outputs. About years ago, resting and action potentials were first recorded from frog sciatic nerves with a differential rheotome Bernstein, Almost 80 years ago, the first intracellular recording of individual neurons was achieved in the squid giant axon with glass microelectrodes Hodgkin and Huxley, Ten years later, voltage clamp was developed, and patch clamp in the s Cole, ; Marmont, ; Neher and Sakmann, About 60 years ago, implantable microelectrodes were developed to record from single neurons in a freely behaving ground squirrel during 4 days Strumwasser, Nowadays, penetrating multi-electrode arrays MEAs can record from individual neurons simultaneously at multiple sites to study distributed neural circuits Gehring et al.

Yet, even so, the huge number of neurons and the complexity of neural interactions preclude the high-density parallel recordings of the whole mammalian brain. Almost years ago, experimental manipulation of neural activity began with electrical stimulation of nerves.

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The first electrophysiological experiments were achieved in frog neuromuscular preparations through electrical stimulation of sciatic nerves by using electric machine or atmospheric electricity during lightening Galvani, Electrical stimulation provides high temporal resolution and can be used in humans to modulate neural activity, such as deep brain stimulation, introduced in clinical practice in the s to treat psychiatric disorders such as schizophrenia Delgado et al.

Multi-electrode arrays were developed in the s to record and manipulate neural activity in living laboratory animals Strumwasser, and are evolving towards chronic, large-scale recording and stimulation at the single-neuron level in freely behaving animals Fu et al. Optogenetics, developed in the early 21st century, has been generalized during the last decade to test and generate hypotheses on brain function in non-human neuroscience, using genetically encoded light-activated proteins to manipulate cell activity with cell type-specific and high temporal resolution Zemelman et al.

Nevertheless, it is extremely challenging to control separately all of the cells in the mammalian brain with high spatiotemporal resolution during behavior, particularly due to light scattering and power deposition requirements Deisseroth, Noninvasive approaches such as EEG and MEG are suitable for human studies and long-term monitoring of brain activity, but their low spatial resolution precludes studies at the cellular level Babiloni et al.

Efforts are underway to measure at the cellular level brain activity in persons carrying recording or stimulation electrodes or neurotechnological devices for therapeutic applications or experimental studies, such as deep brain stimulation and brain-machine interface Moran, ; Lozano and Lipsman, However, these studies are not scalable to large-scale monitoring.

Noninvasive stimulation techniques for human studies usually activate brain areas on a centimeter scale, such as transcranial magnetic stimulation, introduced in to stimulate the human motor cortex for neurological examination Barker et al. These techniques lack accuracy and specificity. Over the past years, experimental studies trying to unravel the causal relationship between neural activity and behavior have evolved from recording and stimulating nerves in frog neuromuscular preparations to chronic monitoring and manipulation of individual neurons in freely behaving animals, from electrical stimulation and ablation techniques to optogenetic manipulation with cell type-specific and high temporal resolution, from univariate correlation between brain regions and behavioral stereotypes to large-scale multivariate monitoring and manipulation of neural circuits, with the ultimate goal of producing a dense functional map of the dynamic brain Insel et al.

However, to demonstrate the causal relationship between neural activity and brain function, dense functional mapping requires in principle a comprehensive map of the connectome and the parallel recording from the interacting molecules, cells, circuits and areas throughout the brain.

Even with technological advances, dense functional mapping of the whole brain is extremely challenging and thus considered by many researchers to be science fiction Shen, How can we overcome this challenge to identify all the molecular and cellular mechanisms underlying brain function and behavior? Quantifying behavior is a major challenge to studies that aim to identify the neural correlates of pre-defined classes of behavioral stereotypes, from the movement of a limb to decision making and emotions Blakemore and Robbins, ; Koelsch, ; Uhlmann et al.

Such behavior classifications do not necessarily correspond to inherent behavior structure constrained by biophysics and neural activity, and they preclude the identification of intrinsic neural mechanisms that give rise to behavior—the output of the functioning brain as an integrated system. Automated behavior quantification and classification using techniques such as machine vision and learning to extract representations of stereotyped behaviors are the first steps towards objectivity and consistency in behavior classification and have the potential to reveal behavioral patterns overlooked by human observers, although these approaches are still based on assumptions and biased Hong et al.

Dense functional mapping is producing huge amounts of data, ranging from molecular and cellular interactions to the connectivity between brain regions and behavioral outputs. Network-based approaches propose to analyze these big, complex data and to model brain networks with theoretical and computational methods such as graph theory and algebraic topology, through statistical inference and dimensionality reduction Bassett and Sporns, Although these approaches have the potential to uncover structural and functional features of brain activity, they are subject to methodological and interpretational limitations that result from uncertainties in data acquisition and network definition, thus requiring sophisticated, neurobiologically based brain models down to the molecular scale to reveal the mechanisms underlying brain function and behavior Sporns, ; Medaglia et al.

Organism-level behavior emerges from the interaction of structural connectivity and signaling processes at the molecular, cellular and circuit levels, involving the dynamic activity of huge numbers of molecules and cells as well as multiple physiological and biochemical systems. It is the output of the functioning brain as an integrated system. How can we avoid assumptions in behavior classification that bias our research on the causal relationship between brain structure and function?

How can we overcome the barriers of scale and complexity to trace the causal chains of events leading from molecular and cellular mechanisms to brain function and behavior? Over past millennia, brain research has evolved through philosophical, experimental and theoretical phases, all of which have contributed to the development of modern neuroscience. Great achievements have been realized in neuronal mapping, connectivity mapping and functional mapping, but these endeavors are hindered by the barriers of scale and complexity.

How can we scale up cellular phenotyping and deal with the dynamics of cellular properties to achieve a reliable neuronal cell-type classification? How can we rise to the challenge of volume, time and dynamics in full connectome mapping? How can we identify the molecular and cellular mechanisms that give rise to brain function and behavior? To overcome these fundamental barriers, brain research has to shift to a new phase. Simulation neuroscience aims to fill the gaps in our knowledge of brain structure and function through building a digital copy of the brain with predictive methods, by combining experimental and theoretical approaches Markram, ; Markram et al.

It has the potential to overcome the challenge of scale and complexity. The following pages are aimed at exploring the historical roots of this endeavor by identifying the major milestones that are the most related to it and that are capable of characterizing it in a concise way, instead of conducting an exhaustive survey of all the investigators whose important work has contributed to the evolution of modeling and simulation in neuroscience. Neuroscience originated in a nerve, while detailed simulation in neuroscience began with an axon. Action potentials were already measured in frog nerve-muscle preparations more than years ago du Bois-Reymond, , but how is the action potential generated?

Since the first measurement of action potentials, the molecular mechanisms of action potential generation had remained an open question over the following years. More than 60 years ago, two neuroscientists managed to insert voltage clamp electrodes into a squid giant axon and measured the flow of electric current through its surface membrane Hodgkin and Huxley, On the basis of their experimental data and inspired by cable theory rooted in the 19th-century model of signaling through submarine telegraph cables Thomson, , they built a mathematical model of ionic currents to quantitatively account for conduction and excitation and simulated the action potential on the Cambridge University computer.

Simulations showed how potassium and sodium ion channels could generate the action potential and predicted the electrical behavior of the axon consistent with experimental data. This was the first detailed digital simulation of a physiological property of a neuron. Cable theory was further developed to take account of dendritic branching that largely affects neuronal processing. This endeavor gave birth to the first multicompartment dendritic neuron model, based on anatomical and electrophysiological data and simulated on an IBM computer Rall, , ; Segev and Rall, , which was further developed in the following years to unravel the role of dendrites in information transmission Segev and London, Single neuron models then evolved into neurocircuit models to study the activity of neuronal populations and synaptic connectivity.

The development of supercomputers in the s drove large-scale simulation of detailed neuron networks, which made it possible to study collective neuronal activities and the neural mechanisms underlying certain brain functions. Six years later, a network of multicompartment hippocampal neurons with different types of cellular interactions was simulated on an IBM to analyze in particular the mechanisms regulating neuronal synchronization in epilepsy Traub et al.

At the same time, began the early efforts to simulate neurocircuitry underlying vertebrate behavior, in particular simulation of a segmental network of inhibitory and excitatory interneurons underlying locomotor behavior in lamprey, using Rall neuron models with one soma and a three-compartment dendritic tree, which unraveled the cellular bases of segmental pattern generation, including central and sensory mechanisms and the immediate supraspinal mechanisms initiating locomotion Grillner et al.

During the same period, was released the GEneral NEural SImulation System GENESIS , a simulation environment for constructing realistic models of neurobiological systems from subcellular processes and individual neurons to networks of neurons and neuronal systems Wilson et al. In the following years, simulators such as MCell and STEPS were developed to simulate biochemical signaling pathways at the molecular scale Stiles et al. As detailed models of neural systems have become more and more sophisticated, efforts are underway to develop a language that provides a common data format for defining and exchanging descriptions of these detailed models, such as the NeuroML project which aims to develop an eXtensible Markup Language XML based description language Goddard et al.

In parallel with the development of simulators, large-scale simulations continued to grow. A single-column thalamocortical network model with 3, multicompartment neurons, including seven cell types characterized by different types of morphology, connectivity and electrical behavior, was simulated on a Linux cluster IBM e to particularly address the physiology of network oscillations and epileptogenesis Traub et al.

Although the model exhibited gamma oscillations, sleep spindles and epileptogenic bursts, it was insufficient to describe other neuronal network behaviors, particularly due to the omission of many cell types, many unknown structural details, the absence of synaptic plasticity and the restriction of the model to a single column. They considered that detailed modeling of extensive brain circuits was necessary for understanding brain function and for making important experimental predictions that would not have been made without the model.

These previous endeavors mainly aimed to build models to reproduce certain brain functions or dysfunctions, such as action potential generation or neuronal synchronization in epilepsy. However, to trace the causal chains of events leading from molecular and cellular mechanisms to diverse brain functions and behaviors, biologically realistic dense reconstructions of the brain realized without the goal of fitting the model to any specific function if reconstructions are correct, functions should arise naturally are demanded.

This need led to the birth of simulation neuroscience in the early 21st century Markram, Since then, digital reconstructions have increased in size and biological accuracy to unravel deeper mechanisms underlying brain function. This digital reconstruction is able to generate emergent network activity and to reproduce an array of in vitro and in vivo experiments without parameter tuning, and it enables experiments so far impossible either in vitro or in vivo Markram et al. Since its origin, detailed simulation in neuroscience has evolved from a single cell type to more than cell types characterized by morphological and physiological features, from one type of synaptic connectivity to the predicted anatomical and physiological properties of all the intrinsic synapses formed onto and by any neuron, from specific models aimed at reproducing certain forms of neuronal activity to generic dense reconstructions of brain regions with various neuronal activity patterns and emergent network behaviors, from an action potential generated through a squid giant axon to diverse network behaviors of rat neocortical microcircuitry with 31, neurons connected through 36 million synapses.

A large body of disconnected experimental datasets and knowledge accumulated since the origin of neuroscience have been integrated into a unified digital copy of neocortical microcircuitry, allowing deeper insights into the neural mechanisms underlying brain function.

Novel multiple testing procedures for structured study objectives and families of hypotheses

Efforts are underway to reconstruct more electrophysiological and biochemical mechanisms and to simulate the human brain. Simulation neuroscience identifies strategic data and formulates principles of brain structure and function to accelerate our understanding of the brain, instead of experimentally mapping all the elements and activities in the brain, which is impossible to achieve due to their scale and complexity Markram, ; Markram et al. Reconstructions of single neurons are the building blocks of the digital brain.

In the early years of simulation in neuroscience, some researchers were aware of the importance of describing the detailed structure of neurons to simulate the voltage response to inputs impinging on the cell in different locations and interactions between cells generated by extracellular current flows. They were also aware of the importance of reconstructing the diverse types of electrical behavior of neurons. Therefore, they argued against using point neuron models Traub et al.

Nevertheless, at this stage, the endeavor to digitally reconstruct the morphological and physiological types of neurons was limited in scale and accuracy, so new approaches were to be developed. Historically, neuronal morphologies were first qualitatively described through visual inspection, then quantitatively described based on morphometric parameters.

Since these methods are not standardized to objectively describe complex branching patterns of neuronal trees, topological methods have been developed in simulation neuroscience to rigorously quantify the structural differences of neuronal trees and to classify neurons into distinct morphological types by encoding the spatial structure of each neuronal tree with a unique topological signature Kanari et al.

Then cloning each morphological type with statistical variations allows scaling up the reconstruction of neurons belonging to each morphological type while respecting biological variability. Automated statistical analysis can reveal distinctive electrical types; computational multi-parametric approach can extract combinatorial expression rules of ion channel genes underlying electrical phenotypes; ion channels can be automatically inserted by simulators combined with an automated fitting algorithm.

These methods developed in simulation neuroscience allow objective and high-throughput reconstruction of electrical types Khazen et al. The high-throughput digital reconstruction of different types of neurons can be extended from morphological and electrophysiological features to other dimensions such as projection and molecular features when sufficient data that allow quantifying these features become available. Furthermore, the structure and function of brain cells vary according to their position in the brain; this should be considered while reconstructing different classes of brain cells.

To support this endeavor, whole-brain cell atlases are being built, providing insights into cellular organization only possible at the whole-brain scale. During the evolution of simulation neuroscience, the digital reconstruction of different types of neurons has become more and more multi-constrained, realistic and high-throughput, and it allows evolving current neuronal classifications Deitcher et al.

Today, we have objective classification of morphologies which is helping define morphological types; we have more or less agreed electrical protocols that can be used to describe electrical types; we have tracing studies that are helping define the projection types, and we have single cell transcriptome data that are beginning to describe the genetically different types of cells.

Efforts are underway to define a minimum sample size capable of reliably revealing distinct types of brain cells, to reduce dimensionality by defining a relevant level of granularity and to identify permanent molecular features that maintain cell identity—a step forward towards an objective and comprehensive classification of neuronal types. About 80 years later, trying to digitally reconstruct neuronal circuits, some researchers considered pointless to explicitly specify all the neuronal connections, which is unattainable experimentally Traub et al.

They chose to reconstruct neuronal connections by a series of random choices, based on the statistical properties of the network topology, such as the average number of inputs or outputs per cell and the probability of connection between pairs of cells. New approaches based on statistical modeling and synaptic rules have been developed in simulation neuroscience to accurately predict synaptic connectivity Perin et al.

With these approaches, it is possible to predict the number and location of all synaptic connection types shown experimentally and connection properties impossible to measure experimentally such as the number of source and target cells and synapses Markram et al. The physiology of synapses can be predicted by formulating rules of synaptic types based on experimental data to generate a relatively complete map of synaptic dynamics Markram et al. Synaptic plasticity rules can also be formulated Kalisman et al. In this way, it is possible to predict the anatomical and physiological properties of all the intrinsic synapses formed onto and by any neuron.

These predictions combined with future experiments could be used to further refine connectivity reconstruction and simulation. To identify neural mechanisms that give rise to emergent complex behavior, reconstructing and simulating neurons embedded in microcircuits, microcircuits embedded in brain regions and brain regions embedded in the whole brain is an approach consistent with the biological reality that organism-level behavior is the output of the functioning brain as an integrated system, from molecular and cellular interactions to connections between neurocircuits and between brain regions.

Neurorobotics, combined with digital reconstructions, creates new possibilities for studying neural mechanisms leading to emergent behavior across different spatiotemporal scales Falotico et al. The deep relationship between structure and function that guided the first investigators at the origin of neuroscience is the foundation of simulation neuroscience.

Recent digital reconstructions and simulations of rat neocortical microcircuitry could reproduce the spatial mode and the temporal dynamics of empirically observed functional networks without parameter tuning and showed emergent network states modulated by physiological mechanisms Markram et al. In the same reconstructions, a new algebraic topology approach revealed that synaptic networks contain an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity, showing a formal link between neural network structure and function Reimann et al. Our understanding of brain structure and function is being deepened through building a digital copy of the brain.

The dense digital reconstruction of the brain from sparse, complementary datasets by predicting biological parameters that are not available experimentally involves dealing with the relationships between known and unknown parameters, deriving principles from experimental data, and reducing biological complexity while preserving the principles of brain structure and function.

Initial digital reconstructions need to integrate more types of neural mechanisms and signaling systems, such as neuro-glio-vascular unit and neuromodulation Jolivet et al. They will be challenged and refined by new experimental observations.


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As more types of datasets and parameters are to be integrated, more relevant biological principles have to be derived, and programming complexity will largely increase. Efficient computational methods have to be developed to satisfy the requirements of this nascent science in rapid evolution.

Simulation neuroscience is rising to these challenges and constitutes an essential phase of brain research towards transcending scale and complexity to causally link molecules, genes and cells to brain function and behavior. Simulation neuroscience is an efficient approach to integrating disconnected datasets and knowledge in neuroscience that have been accumulated over hundreds of years.

The extraction of the rules of the relationships between datasets that concern different levels of brain organization helps to build an integrated view of brain structure and function Tiesinga et al. Through digital reconstructions and simulations, researchers can conduct in silico experiments, improve experimental methods, test and generate hypotheses and theories, make predictions and suggest new experiments Druckmann et al.

Neuromorphic computing uses very-large-scale integration VLSI systems containing electronic analog circuits to mimic neuroarchitectures of the nervous system Mead, This approach has the potential to overcome the major limitations of traditional computing, such as energy consumption, software complexity and component reliability. Current neuromorphic computing consists in large-scale simulations of neuronal connectivity with few biological details Furber et al.

This research field would benefit from simulation neuroscience, which has the potential to provide the blueprints of neurocircuits. Without deeper insights into the fundamental mechanisms underlying brain function, we cannot effectively treat neurological disorders, which result from dysfunctions of neural systems down to the molecular scale. Today, there is still no effective treatment The Lancet, How can we treat a brain disease if we cannot identify its underlying mechanisms and clearly define it?

How can we restore brain dysfunctions if we do not even understand the neural mechanisms underlying normal brain function? Our understanding of brain structure and function is being deepened as we build and refine a digital copy of the brain. Each step unravels new aspects of brain structure and function in a systematic manner. Even though an accurate and complete reconstruction and simulation of the human brain will require at least yottaflop 10 24 flops computing or even more 1 , we are getting closer to a comprehensive understanding of the brain by developing multiscale simulations.

According to the nature of the studied question, some parts of the brain can be simulated at low resolution, and others at high resolution. This allows accelerating our understanding of the brain even before enough computing power becomes available. Finally, it would be possible to trace the neural mechanisms leading to the emergence of biological intelligence and to challenge the foundations of our understanding of consciousness through building a digital copy of the brain.

Since the dawn of neuroscience, hundreds of years ago, this human endeavor has fundamentally been a series of reconstructions: reconstruction of the neuron as a single cellular unit; reconstruction of neurons into distinct types according to their morphological, electrophysiological, biochemical and molecular properties; reconstruction of neural connectivity between brain regions, neuronal groups, individual neurons; reconstruction of the neural mechanisms underlying brain function and behavior.

In attempting to complete the reconstruction of brain structure and function, experimental and theoretical approaches are hindered by the fundamental barriers of scale and complexity. Leveraging high performance computing, data analysis and statistical inference methods as well as algorithmic approaches, simulation neuroscience quantifies, integrates, scales up and accelerates all the previous reconstruction processes and evolves them into a unified digital copy of the brain—a quantitative and qualitative shift through the dense digital reconstruction and simulation of the brain from sparse experimental data, with the aim of causally linking molecular, cellular and synaptic interactions to brain function and behavior Figure 5.

Since the first observation of nerve fibers, the microscopic and physiological reconstruction of the neuron as an independent cellular unit had taken almost years, while the evolution from the first digital reconstruction of the action potential to the dense digital reconstruction of neocortical microcircuitry took about 60 years. What will the future hold for the reconstruction and simulation of the entire brain? From the dawn of human civilization, the advances in brain research have been generated through a series of fundamental shifts in the types of human thinking to understand the mind and the brain.

Relying on intuitive and analogical thinking, ancient philosophers tried to address fundamental questions but were unable to provide empirical evidence. Seeking evidence, reductionist thinkers in experimental neuroscience have gained a deep understanding of many components of the brain but have also produced a huge number of disconnected datasets and knowledge.

Theoretical neuroscience applies abstractive thinking to be free from the details in the brain, which may advance artificial intelligence but leaves open the question whether it will advance our understanding of the causal links between brain structure and function. To transcend these barriers, brain research needs a new way of thinking and a new approach. This new phase is proposed to be simulation neuroscience, which is based on integrative and predictive thinking. Will simulation neuroscience be able to go deep enough through multiple different layers to finally understand the multiscale brain, to answer the probably ultimate question for us, humans, of understanding ourselves, which has haunted us since the dawn of time?

Atoms are combined into molecules; DNA molecules are bound into sequences to produce genes; genes produce proteins; different combinations of proteins produce various types of cells, which are combined into different brain regions to finally form the unique human brain. How do these complex mechanisms interact, leading from single atoms and molecules to brain function and behavior? How does the brain create our small world immersed in the universe? How does the brain incorporate our experiences that define our existence?

Still so many unsolved questions. After thousands of years of brain research, hundreds of years of neuroscience, we remain strangers to ourselves. To understand the multiscale brain, neuroscience now has to shift to a new phase. XF and HM conceived the research and wrote the text. XF wrote most of the text and made all the figures. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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An Introduction to this Retrospective Article

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Uncovering the structure of self-regulation through data-driven ontology discovery

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Creating a research question can be a difficult process and one which may not be perfect the first, second or even third time you try. Creating a succinct and thought provoking question which is precise in its aim provides the researcher with explicit aims and targets to work towards. With a non-specific and vague question research can lose its focus and confuse readers and be of no benefit to the scientific community. To make the difficult task easier specific step-by-step have been created such as the following. The SPIDER [7] technique is to qualitative research, what PICO is to qualitative research; it is an acronym to aid the budding researcher to develop a concise and direct research question with clear aims and direction.

The official definition is [7] :. The results were promising with a more manageable number of results yield by SPIDER however some discrepancies were seen in the search results. As with quantitative research there are ethical standards which need to be upheld when performing qualitative research. The starting point of ethical concerns are the 4 principles of Beauchamp and Childress [8] :.

It is important to consider that asking a person about their thoughts and feelings about an experience may be traumatic or emotionally distressing and care needs to be taken when asking these questions. It may require a talking-therapy aftercare service to be in place to address these concerns. It is also important to remember that the emotions or stress may arise after the research has finished. With qualitative research it is vital to consider confidentiality you could potentially have thousands of words typed from a conversation you have had with a participant, potentially containing sensitive information so password protecting document and keeping them under lock-and-key is essential [13].

This may contain individuals from a wide range of professions, ages and experience who are separate from, and not involved in, the study in question. Universities and research centres will have their own research committees. If a study involves any of the following ethical approval will be needed [14]. Sampling in qualitative research is integrally different to quantitative research sampling.

This is explained in the following subheadings. Quantitative research is focused on generalizability and so large numbers of participants are required. This may be 's of participants to ,'s. However in qualitative research all that is needed is enough participants to answer the research question. More participants may be recruited half-way through the study or until common themes or answers reoccur Data Saturation , this may only take 20 participants. Another consideration is the sheer amount of time and effort required to thoroughly analysis and manage qualitative data, so this factor may be limiting to the scope of the study [5].

Convenience sample is the least rigorous technique in qualitative research, essentially it is involving the most accessible subjects. Although being the least rigurous it is most cost effective financially and in terms of effort and time demands. It may lack credibility so a more thoughtful and thorough method is needed [5]. The researcher seeks out participants who will answer the research question the most effectively.

It may be beneficial to include a narrow or broad sample based on intellect, geographical location, gender, age, experience or beliefs. Participants may also be able to suggest other participants who have had similar experiences which further the sample size, this is known as snowball sampling [5]. Theoretical Sampling necessitates building interpretative theories from the emerging data and selecting a new sample to elaborate on the new theories may be needed.

This sample is ever changing until the research questions are answered [5]. There are several different ways of collecting qualitative research data. The below subheadings contain the most common methods and will provide an overview into each. Group interviews have been around since the early 20th century and can be seen in a study performed by Bogardus in [15].

The method is used in a wide range of study types from mass communication, health, spirituality and education [16]. Sometimes it can be difficult to choose between one-to-one interviews and grup interviews, as seen above they are both versatile and have many uses however group interviews can take the data to the next level. Blummer explains this [17] :. This is true, a group interview is seen as more ' naturalistic' than its more structured cousin and lots of data may be uncovered.

The group environment may encourage others to take part as it may feel more natural than a interview setting. A reflective thought may be provoked in some individuals further enhancing the data. There are a number of different types of group interview that are suitable for different research questions or methodological approach, these include brainstorming, nominal group techniques and focus groups [18].

These could each have their own page on Physiopedia and maybe one day they will, however for now further research will be needed by the reader. In a sub-section about sampling was discussed, now different considerations need to be considered with group interviews.

If there are too many participants then it may be difficult to control the discussion, too small and the discussion will not be insightful enough, therefore participants is advised [19] ,. Another way to gather information is by observing people or events in their natural environment.

Background

It is important to understand the implications of watching people and ethics may play its part depending on whether the observations are overt subjects know they are being observed or covert it is done without knowledge or consent. The three main types of observation are:. Action research is a reflective process that requires an individual to work with a team to not only find solutions to problems but also to improve the way the team works to solve problems.. There are many other alternatives to the above mentioned methods of data collection. A lot of these online methods can be called ' Remote Interviewing' [16].

You may have 10, words from each individual interview, or 50, from a group interview to try and decipher ideas from. There are a number of different ways of analysing the data, there can be a thematic, descriptie or in-depth approach, as well as others. However thematic analysis is usually sufficient [20]. Before you get to the stage of finding new ideas and answers from your collected data you need to decide what you are going to do with it, are you going to fully or partially transcribe it.

Once you find your preferred method of transcribing it is important to be consistent throughout your work. The more information you can gather, the more patterns emerge giving you rich and thick description which is integral to and the focal point of qualitative research. Some conventions of transcribing are as follows [21] :. Over the past decade or so, as qualitative research has gained support transcribing has become a prominent area of discussion, particularly in relation to the quality of transcription and errors which can arise [16]. Inaccurate transcription can have a very detrimental effect on a research piece.

If information is lost or misrepresented then the credibility of the work disappears. Recording quality is something which should be a relatively simple area basic improvement can be made. Making sure the recorder is of high quality, microphones being suitable places, voice is clear and loud and the pace of speech is at a measured pace.

Remember you can always, if something is deemed important, ask the participant to repeat is clearer and louder. It is also good practice to record salient information at the start f the recording such as time, date and who the participant is so it is easily identifiable [16]. Missing context is something that is a little more troublesome to overcome. Essentially this is an area which will become a problem if you are using simple verbatim transcription techniques.

If you area just typing up what is being said and not the facial expression or tone of speech then information can be misconstrued. If for example you were talking to someone about their new job and they said "its great, great, I love it" you would assume they were having a great time. However if you included sarcastic tone and facial grimace then a completely different message is construed. It is therefore important to include these when necessary.

Tidying up transcripts is something easy to accidentally do. Spoken English is very different to the written form. It is always messier, broken and the pace looks odd when written down. It is always tempting to correct mistakes when transcribing the information however it is not the job of a transcript to do this. Perhaps phrases such as inaudible - perhaps "time of day" or unclear, a name would be more accurate and suitable for transcription purposes.

Technical terms are also challenging and abbreviations should be avoided. Ensure the participants avoid them, or if they do, be sure to ask what they mean. If they are unavoidable a glossary of terms would be appropriate. Thematic analysis reviews all of the data to identify common ideas which reoccur and identify these as themes that summarise the collected views of participants. The idea of this stage is to begin to identify the parts of the transcripts which will most likely help to answer your research question. It is always a good idea to read through the transcript several times to familiarise yourself with the work so as not to lose context when referring back in the future and to understand the whole meaning.

Next highlight anything you feel will help you understand the views, opinions and beliefs of the participants and add comments next to them to explore the findings further. A colour scheme or numbering may be beneficial to connect ideas together and make it easier to find them. Double spacing the transcripts may help de-clutter the page and allow room for notes or using an annotation software package. Once you have lots of comments and highlighted text, you may find codes emerging from the comments. Make sure they are close to the data and avoid speculation.

Try not to incorporate every single piece of text or comment but only the salient points and use short phrases or abbreviations for your themes such as: A-Level Chemistry or Self-confidence building. You can merge comments and codes if they are reoccurring or if they overlap considerably. This process is then repeated with the next transcript, adding the interview extracts, comments and themes to your already collected data.