A Brief History Of Human Brain Mapping Pdf Free
Jaiker Edouard
Results: There were important milestones leading to the incorporation of ECS into practice: 1. Aldini's (1802) first known stimulation of exposed brain to defend Galvani's views on excitability in the frog-leg experiment against Volta's, ironically by employing the Voltaic pile. 2. Animal experiments in the 19th-century to study the brain and to optimize the procedure: Rolando (1809) reported on motor induction, Fritsch and Hitzig (ca. 1870) introduced the concepts of bipolar and threshold stimulation, and Ferrier (1873) generated reproducible homunculi in animals. 3. Parallel to 2, advances were made based on clinical observations by Bravais, Todd, Jackson, and Broca among others. 4. First known stimulation in conscious humans by Bartholow (1874) led to catastrophic outcomes. Horsley (1886) performed first intraoperative stimulation on Jackson's epileptic patient. 5. Advances accelerated in the first-half of the 20th century with Cushing (1909) performing first awake-craniotomy eliciting sensory responses to Penfield's work culminating in standardization of clinical use and generation of detailed maps including the famous sensory-motor homunculi. Parallel advances in instrumentation were made from the Leyden jar (1745) to present customizable current-controlled stimulators.
Conclusions: ECS is commonly used in neurosurgery for localization of brain functions and is the benchmark for research studies. Significant leaps have been made since ECS first used in the 19th century. It evolved to remain the gold standard for localization of human brain functions in the 21st century.
Human functional brain mapping as we presently know it began when the experimental strategies of cognitive psychology were combined with modern brain-imaging techniques (first positron emission tomography and then functional magnetic resonance imaging) to examine how brain function supports mental activities. This marriage of disciplines and techniques galvanized the field of cognitive neuroscience, which has rapidly expanded to include a broad range of the social sciences in addition to basic scientists interested in the neurophysiology, cell biology and genetics of the imaging signals. Although much of this work has transpired over the past couple of decades, its roots can be traced back more than a century.
While many of us had hope that 2021 would be back to "business as usual," it instead offered a chance to re-evaluate our relationship to one another and to the larger systems we find ourselves in. The OHBM Blog, too, often took a reflective tone, with posts on the ongoing work from the brain mapping community as well as the past, present, and future of how we communicate these efforts. Read more.
From the 1960s there was an explosion of neuroscience research. With rapid advances in technology, and collaboration across fields such as physics and genetics, scientists have made great leaps in understanding the brain, through detailed imaging and mapping of networks (pictured), and deciphering chemical pathways.
Experimental and theoretical phases have developed through three main paths: neuronal mapping that tries to classify and catalog different types of cells in the brain; connectivity mapping that aims to map connectivity between individual neurons (neighboring neurons, neurons in neighboring groups, neurons in distant brain regions), between groups of neurons (layers, columns, nuclei, etc.) and between brain regions (visual area, auditory area, etc.); functional mapping that tries to relate brain function and behavior to the structure of the brain (e.g., role of partial connectomes or the whole connectome).
Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes (Gall and Spurzheim, 1810; Vogt and Vogt, 1903; Brodmann, 1908; Sporns, 2016), from observing and characterizing brain responses to stimulation (Hitzig and Fritsch, 1870; Penfield and Boldrey, 1937) towards understanding the causal relationship between neural connectivity and brain function (Bassett and Sporns, 2017; Reimann et al., 2017a). Today, at the cellular level, neuroscientists are still surprised to find that different neurons respond to different inputs in a different manner and are still composing an endless spectrum of stimulus preference maps for neurons, while we are moving from considering only how the type of neurons is responsible for their different responses towards identifying the contribution of the underlying networks. At the whole-brain level, studies are beginning to reveal how the underlying connectome shapes, for example, functional magnetic resonance imaging (fMRI) image patterns. At the behavioral level, attempts to map signatures of specific cognitive functions to the underlying structures are still limited to networks of brain regions. As the number of brain regions found to be involved in any cognitive task grows, functional mapping will likely evolve from statistical subgraphs of the brain towards dynamic full graphs.
However, in these three paths, experimental and theoretical approaches 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 comprehensive census of cell types in mammalian brains? How can we rise to the challenge of volume, time and dynamics in full connectome mapping potentially even down to the nanoscale? How can we trace all the molecular and cellular mechanisms that give rise to brain function and behavior?
To better visualize the trajectory of neuronal mapping in the future, we need to understand its origin. What is the history of the neuron, from its first descriptions to modern neuronal classification? First of all, how did humans discover the neuron?
The storage and processing of gigantic volumes of data are problematic (Schreiner et al., 2017). The first fairly complete reconstruction of the C. elegans nanoconnectome required 10,000 EM images (White et al., 1986). Recent local circuit mapping by EM has high data output rates of gigabytes per minute (Helmstaedter and Mitra, 2012). At synaptic resolution, a human brain may require 2 million petabytes (Swanson and Lichtman, 2016). And this is just for the anatomical data, but what if we include the electrophysiological, biophysical and biochemical counterparts?
Although section preparation automation techniques such as SBF (serial block-face) SEM (Denk and Horstmann, 2004) and ATUM (automated tape ultramicrotomy) SEM (Schalek et al., 2011) were invented and data acquisition has been accelerated through parallel image processing (Eberle et al., 2015), the automation of large-scale image segmentation and reconstruction remains the fundamental bottleneck for EM. Methods such as machine learning and crowdsourcing are gradually reducing the problem (Kim et al., 2014; Greene et al., 2016; Staffler et al., 2017), but no existing computational segmentation algorithm is accurate enough to completely replace human annotators. A recent reconstruction of the nanoconnectome of 950 neurons in the mouse retina took 30,000 h (Helmstaedter et al., 2013). At current speeds, the complete reconstruction of the nanoconnectome of the human brain may require 14G person-years (Plaza et al., 2014).
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., 2009; Wendel et al., 2009). 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, 2010; Lozano and Lipsman, 2013). 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 1985 to stimulate the human motor cortex for neurological examination (Barker et al., 1985). These techniques lack accuracy and specificity.
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, 2017). 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, 2014; Medaglia et al., 2015; Bassett and Sporns, 2017).
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.
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