Mapping Cognition to Brain: brain localization

Mapping Cognition to Brain: brain localization revisited1 Probably the best known quote in cognitive neuroscience came from Marvin Minsky, who stated: ‘the mind is what the brain does’. Its implication seems clear: those who seek to understand the mind should turn to the brain for the real answers. More recently a Dutch neuroscientist even went one step further and published a best-selling paperback book under the title ‘We are our brain’ (Wij zijn ons brein, Swaab, 2011), equating not the mind, but our first persons sense of identity with our brain. Which raises the interesting question if ‘we’, that is the same biological structures that generate mind are also capable to unravel their own inner structure. This paradox is a good example of what cognitive scientist Douglas Hofstadter called a ‘strange loop’: a never ending recursive process. A groundbreaking insight from modern neuroscience that seemed to provide at least the beginning of a solution of the philosophical paradox was that the brain is not just an organ but also a network. Although the structure of the brain is enormously complex it still remains a biophysical system which operations are largely based on algorithms. An algorithm is a series of unambiguous steps to solve a problem, which in case of the brain are called neural computations. Human brains are molded by adaptations and mutations over millions years of evolution, and not basically different from that of other mammals. Evolutionary biology and computational neuroscience have also provided new insights in the building blocks of the brain. This reveals that its inner workings are largely based on networks involved computations and representations, processes involved respectively in transformation between input and output, and storage of its informational content in neural codes, very much like computer networks (see figure 1). If the working of our brain is indeed algorithmic, so will be its product, our mind. And if the human mind is indeed implemented in the brain, and brain patterns could be analyzed in a sufficiently fine-grained way, it would be a question of time for artificial intelligence engineers to build machine software working as minds, as predicted by modern futurologists. Some neural engineers now even consider uploading the whole brain to a computer a realistic option for the future (see Kurzweil, 2005, Koene et al, 2009). Input (perception) Analysis and synthesis of stimulus characteristics

Posterior cortex

Central (memory) Storage, comparison, retrieval, stimulus- Posterior and response translation, decision anterior cortex Output (motor system)

Action programming, instruction generation, motor execution

Anterior cortex

Figure 1: A simplified list of computations and associated cortical regions

Global versus local functions Back to the present: despite the ever growing volume of data generated by structural and functional neuroimaging of the brain, cognitive scientists are still struggling with the question how the human mind emerges from that lump of billions neurons and their connections. How can the human mind -even if we assume that its 1

Parts of this paper are based on a translated transcript from my Dutch Textbook (Kok, A., 2016)

1

functional architecture is basically algorithmic- be mapped to the brain? The brain localization debate, initiated in the 17th century when René Descartes launched the first brain theory called Dualism, is even more actual in our 21nd century. The mind-brain puzzle is not only a subject of current philosophical debates, but has also proven difficult to solve empirically. This is because cognition (our ‘knowledge of the world’ and ‘initiator of our actions’) is not a unitary function but a very mixed bag: a ‘cognitive jumble’ indeed. Observation of natural behavior, although rich in its content, cannot be the solution. Since natural behavior is a direct reflection of the cognitive jumble, it would not be a good starting point for a scientific study of the mind. Cognition as a global theoretical concept therefore first needs to be decomposed into scientifically tractable sub-domains, that are ‘specific enough to be operationally defined in the context of an experimental paradigm and that can be tied to specific measures of cognitive and neural function’ (Spunt et al., 2017). Fractionation of cognition The central mission of cognitive psychology is to unravel cognition and the ways it affects behavior. The term cognition refers to all processes by which sensory input is transformed, reduced, elaborated, stored, recovered, and used. Together they form the Cognitive Domain which includes (but are not limited) to the following functions: Perception and Action, Memory, Attention, Language and Emotions. These global functions (or: sub-domains of cognition) are not unitary, but involve more specific or local functions (see figure 2). Figure 2: Decomposition of Cognition and Brain (see text for further clarification)

These local functions are not just intuitive categories, but based on numerous behavioral studies that justified their functional separation. Two representative examples are given below. Consciousness is not included in the list in figure 2. This is because in a strict sense consciousness it is not a separate subdomain of cognition, but rather an activated state of other sub-domains like perception, memory, emotion or even the motor system. Each of these functions have implicit (unconscious) or explicit (conscious) manifestations, two terms coined by David Schacter in 1987. Although consciousness is the most hotly debated topic in current psychology magazines, newspapers supplements and best-selling books, it might not be the unitary function as often suggested in these media. When we break consciousness apart, we will discover that it actually represents an amalgam of various ‘sub-states’. To name just a few: wakefulness (being awake; a prerequisite of consciousness), the sense of a self, awareness of the environment, orienting to environmental cues, the autobiographical self and its imaginary content and focused attention (Damasio, 2010). For example, priming and conditioning are forms of implicit memory and learning that take place ‘under the radar’ of consciousness. In addition imagery, often seen as a prototypical manifestation of 2

consciousness, actually represents an active state of a working memory circuit with a ‘central executive’ recruiting perceptual systems in the brain (Baddeley & Hitch, 1994). Interestingly, when subjects perform a visual imagery task like mental rotation, the same areas light up in fMRI scans of the brain that are activated during visual perception proper (Kosslyn& Sussman, 1995). Confirming Baddeley’s working memory model where higher centers recruit the primary perceptual systems. An aside on Consciousness The reason why the state of consciousness has reached the status of an independent entity is that that theorists still disagree on what consciousness really is. Consciousness is a phenomenological concept that, with no reference to objective behavioral or neural processes, easily becomes scientifically intractable or even will be assigned metaphysical qualities. In addition, conscious experiences and the sense of self suggest that consciousness should reside somewhere in the brain, preferably at a strategic central position. Its function would be to integrate and even interpret information from the outside world, like a spectator in the theater. But neuroscience so far has not been able to find such a local centre of coordination or source of awareness in the brain. Instead it seems that consciousness arises from coordinated activity of many different brain sites. In line with this view, Bernard Baars (2003) has proposed that the overall function of consciousness is the availability of a global workspace. The primary functional role of consciousness is to allow a "blackboard" architecture to operate in the brain, in order to integrate, provide access, and coordinate the functioning of large numbers of specialized networks that otherwise operate autonomously. The workspace model was further worked out in a simulated brain model by Dehaene et al (2003). In his model salient sensory stimuli temporarily mobilize excitatory neurons with long-range axons igniting a global activity pattern. The global brain state also links distant areas including the prefrontal cortex through reciprocal connections and inhibits surrounding workspace neurons thus preventing the conscious processing of alternative stimuli. This would include automatically activated processors that contribute to the processing by workspace neurons without entering consciousness. Elements of Dehaenes model seem consistent with our earlier description of shortterm memory as the sum of activated elements in long term store, as well as with Baddeley’s model of working memory. Except that the concept of ‘central executive’ is now replaced by that of ‘global brain state’ recruiting other local and global areas. But this leaves us with the question if consciousness is a cause or consequence? Does the conscious state per se facilitates coordination widespread areas (as assumed in the global workspace model), or is it merely a byproduct of neurons in working space that together exceed a certain threshold of activation.

Memory. The ‘memory cake’ is usually sliced up in explicit and implicit memory, which in turn can be divided in even finer particles such as episodic and semantic memory, skills, priming, conditioning and associative learning. Perhaps the best known split-up of memory is that between short and long term memory (STM and LTM). But instead of assigning different local functions to STM and LTM one could also consider STM as a neural assembly that is temporarily activated within LTM space (see also Cowan, 1995 and Fuster, 1995). This could occur in different ways, depending on the conditions and pathways of eliciting events that cause activation of the neural assemblies. The four prevalent conditions would be: a) sensory priming, b) consolidation, when new information needs to be stored in LTM, c) recall and recognition, when old information has to be retrieved from LTM d) working memory, recruiting representations stored in LTM. Condition a and b would primarily 3

depend on afferent pathways from sensory input, conditions c and d on pathways from internally generated cues In contrast with LTM, which is a passive store of knowledge with immense structural capacity, STM has a much more limited capacity, mostly due to its temporal constraints. The content of STM could also become available for conscious access, if the associated network ignites sufficient neural elements in cognitive space. This would exclude effects of priming that are generated in the sensory cortices, but include most effects of consolidating and retrieval that involve ‘higher’ prefrontal and temporal areas (e.g. Moscovitch et al. 1994). Fractionation of the brain Similarly, in the neural domain gross dissection of a brain reveals that different parts of the brain do different things: like cognition the brain has a distinct hierarchical architecture, running from global to local networks or structures (see figure 2 lower panel). The number of top-down as well as bottom-up neural connections between these structures is simply overwhelming. For example, the three major subdivisions of prefrontal cortex, the dorsal, ventral and medial regions (DLPFC, VLPFC and MPFC, respectively) each connect via reciprocal pathways to different global networks in posterior and temporal cortices, as well as to more local networks more downstream in the hierarchy. Posterior parietal cortex (PPC) in turn connects to areas in anterior cortex as the supplementary motor area (SMA), frontal eye fields (FEF), superior colliculi, but also to the thalamus. Separate anatomical partitions of temporal cortex (the supra-, medial- and infra-temporal gyri) connect with limbic structures like hippocampus and regions in secondary visual cortex. Even the primary motor network in the brain is not a homogeneous area, but rather consist of many sub-regions that each have their own function like controlling our feet, finger and other body parts. Figure 3 Simplified illustration of reciprocal circuits in large scale networks involving higher and lower brain regions and associated sub-domains of cognition.

In summary: the richly branched cortical trees in the brain illustrate the refined hierarchical structure of large scale networks with information streams running from more global to local structures and vice versa (see for a simplified example figure 3). Even local structures like thalamus, amygdala and basal ganglia ‘encapsulate’ various nuclei that connect via specific pathways to their cortical target areas, providing filtered input or supporting the information flow regulated at higher cortical levels. These examples exemplify that the brain is a complex system in which information is continuously processed and transported between different more specialized regions with coherent dynamics. The brain is certainly not equipotential, as Carl Lashley’s experiments 4

on maze learning in rats long ago seemed to suggest in early 20th century. Neither is it a patchwork of specific islands each carrying out their specializations independently of the other. Below, we shall discuss in greater detail the three major principles underlying the functioning of the brains network, namely modularity, connectivity and flexibility. Modularity Modularity relates to macroscopic brain regions or sub-networks that often have a preference for a particular category of stimuli. They represent the more specific processing components of complex global functions that are distributed over a large area of the brain. Within these large networks network modularity captures the extent to which a network has community structure, by dividing the brain into different modules. Figure 4 A distributed network connecting more densely connected local networks or modules (adapted from Newman, 2006)

Modules usually form densely connected groups of nodes, with only sparser connections between groups (see figure 4) . In a more general sense, a module might represent any area in brain in which the ‘nodes’ (clusters of nerve-cells) are more strongly connected to each other, than to nodes in the full brain network. It is certainly not only an isolated encapsulated processing structure that only has access to one specific channel of input, as originally proposed by Jerry Fodor. Some modules in primary visual cortex may indeed show input specificity , while others might show more central specificity. Central specificity would hold for brain regions that have access to several sensory transducers, together with top-down modulation to intelligently filter that information (see Spunt et al., 2017, for further elaboration of this ‘new look’ on modularity and domain specificity). In the more general sense the thalamus, receiving input for sensory channels as well as top-down modulation by posterior parietal cortex, has clearly modular qualities in filtering out information from the outside world. If such a central module ‘lights up’ up in a fMRI scan, it might not so much reflect its specific output, but rather the joint input function that is specified by all the synapses made onto this region from elsewhere in the brain. Connectivity and distributed processing Connectivity relates to the numerous white-matter tracts that allow transmission of neural information between cortical areas. In contrast with modules that show a strong local connectivity and clustering, large scale networks connect localized cortical regions over relative long distances. A popular example is a road map of a country where various routes and highways connect larger cities and smaller villages with dense local connections. Research in cognitive neuroscience has typically focused on identifying the function of individual brain regions. Recent advances, however, have led to thinking about the brain as consisting of interacting sub-networks that can be identified by examining connectivity across the whole brain. In general, little is known about the overall connectivity of the brain and the dynamics of the large scale networks. Interestingly, some studies that used graph theory in combination with resting state fMRI showed that functional connections in the brain were characterized by a small world organization. This implies that local areas, despite their own specialized functions, are only a few connection steps (usually via a few long distance connections) away from each other, thus ensuring a high level of global connectivity across the full network (Sporns, 2004). Of special importance here is the default 5

or resting state network that functionally connects the medial frontal cortex with the posterior parietal cortex, suggesting a high level of functional connectivity between these regions (Grecius et al, 2003). In particular the white-matter cingulum tract could function as the neural ‘backbone’ of this network (see further below). So brain activity is local as well as global, and the keyword that integrates this two principles is distributed processing. Local specialized processes and their respective sub-networks or modules in fact constitute the building stones of large-scale networks. This implies that Figure 5. Illustrative example of distributed processing in a large scale functional network. Global functions A, B and C are each partitioned in three local functions in anterior, posterior and limbic area of the brain. For example, complex function A is not ‘localized’ but recruits three sub-functions a1, a2 and a3 at widely separate locations I,II and III

from a functional perspective the distinction between global and local functions makes little sense. Indeed, complex functions or networks when functionally decomposed, may in fact reveal a pattern of local networks that are spread out over large areas of the brain (see figure 5). An example of research supporting this principle comes from studies of brain-damaged patients showing that a highly localized lesion will only affect the local subcomponent, but not eliminate the complete function. Flexibility of cortical organization. Flexibility in combination with modularity together can make unique contributions to explain task performance. For example modularity might be relevant for performance in simple tasks while flexibility might play a greater role in predicting performance in complex tasks that require cognitive control and executive functioning. An important lesson from studies of neural connectivity is that structural and functional connectivity are not the same. Structural connections in the brain are a prerequisite for functionality and merely provide the topographic framework for neural transmission. In some cases however, functionality is already present as a predisposition in local networks. For example the brain’s ability to recognize visual categories is guided by category-selective ventral-temporal cortex. A recent imaging study has demonstrated that the large-scale category selectivity of ventral visual regions is already present in 6-month-old infants and develops without visual input (like in blind individuals; see van den Hurk et al., 2017). Functional flexibility in larger networks on the other hand, characterizes how regions in the brain may switch allegiance from one module to another over time. In emphasizing a more dynamic view of domain specificity, Spunt et al. (2017) described three different scales of possible change that should be considered: phylogenetic, ontogenetic and truly dynamic (real-time, momentary modulation by attention and context). In early development changes in synaptic transmission (e.g. sprouting and pruning of redundant connections) could arise from partly innate factors. Alternatively, it may also arise later and develop as a result of very specific more culturally determined kinds of experience such as reading and speaking, or practicing specific sensory-motor skills like playing a piano. A special case (and even a prerequisite) of neural flexibility is called ensemble coding. This principle, originally suggested by Donald Hebb, implies that individual neurons could participate in different assemblies of nerve cells, and would thus contribute to multiple 6

different computations in identifying objects (figure 6). A related concept is population coding, a method to represent stimuli by using the joint activities of a number of neurons. Each neuron has a distribution of responses over some set of inputs, and the responses of many neurons may be combined to determine some value about the inputs. This principle was described by Georgopoulos and his colleagues who formulated a population vector hypothesis to explain how populations of motor cortex neurons encode movement direction. Figure 6 Example of ensemble coding. Recognition of objects is the result of activation of a specific pattern of nodes in a network (in black, left). Some of these nodes can also be recruited in different configurations of networks involved in recognition of other objects (right).

Ensemble coding ensures an efficient use of neural space, since a relatively small number over overlapping groups of neurons are needed to carry our neural computations like identification of different objects or making different movements in space (see figure 6). The same principle could also play a role in rerouting of information after structural damage of local pathways or neural assembles (e.g. after acute brain damage) or as a result of more gradual neural loss (occurring during normal aging and manifested in loss of white matter, neurotransmitters, synaptic transmission or even nerve cells). Further in insights into the mechanisms of cortical plasticity could contribute in neuro-rehabilitation in brain damaged patients a as well as mitigating the effects of normal decline in cognitive functioning in elderly subjects. For example gradual cognitive slowing, a typical manifestation of normal aging could result from information rerouted via a longer route due to loss of nerve cells or neural connections in the original pathways. Flexibility also pertains to effects of practice and mental training on functional connectivity, typically described as experience-induced plasticity. On the behavioral level effects of training on performance in reaction and memory tasks are well documented. Some of these studies have suggested that an expert brain needs fewer resources than a non-expert brains to prepare and execute a motor act. Furthermore, practice could suppresses cognitive processes irrelevant for the motor task, while with growing automaticity less cognitive effort is devoted to the motor task (e.g. Gidde et al., 2010). Research on flexibility of the brain still remains a unexplored area, and certainly does not support the optimistic claims of some firms selling commercial brain-training programs. Dynamic rerouting of information during practice could indeed change even the current processing architecture of the brain, for example by making longer functional routes used during controlled task operations redundant. However, well controlled studies on the effect of practice on the brain are scarce and have so far given incongruous results, including patterns of increases, decreases and functional reorganization of regional activations (Kelly & Garavan, 2005). Summing up: bridging the gap between mind and brain One of the most striking developments of the last three decades was the cross-fertilization between cognitive psychology and neuroscience, leading to a new discipline called cognitive neuroscience. The first step in this development was the insight that the architectures of cognition and brain as developed in these two disciplines (as sketched above) had much in common. The 7

next step was the discovery that cognitive space also allowed to be mapped to brain space in a functional sense, both at global as more specific levels. Of particular importance was the input coming from studies of cortical lesions in neurological patients, showing dissociations in specific cognitive functions depending on the site of the lesions in the brain. Another source were functional imaging and computational modeling studies of the brain with normal subjects performing in specific cognitive tasks. Figure 7 Top: the central executive network (adapted from Seeley et al., 2007). Bottom: The default mode (or resting state) network Average of 8 PETstudies (adapted from Buckner et al. 2008). From left to right: medial, coronal and horizontal sections.

While conventional fMRI provides important information on the total amount of brain activity in independent regions, functional connectivity present a picture of the relationship between two cortical regions in the temporal domain. This is accomplished by computing the coherent activity of fMRI BOLD time series. Using these data, network science provides mathematical tools for investigating the structure of the brain network, with brain regions serving as nodes, and the connections between brain regions serving as edges in the analysis. Two examples of products of the recent cross-fertilization of brain and mind are presented below. Attentional control network In experimental cognitive psychology selective spatial attention has often been conceptualized as a mental spotlight enhancing perceptual features that fall within the spotlight, and suppressing its irrelevant surroundings. While human attention studies that used electrophysiological indices had already confirmed the notion that visual-spatial attention enhanced excitability of neurons in extra-striate cortex (Hopfinger and Mangun, 1998), the brain mechanism that controlled this enhancement still remained unknown. Later studies however using fMRI in combination with voluntary cuing tasks were also able to identify these control areas in the brain (see figure7 above). This attentional control network that also became to be known central executive network involved areas in posterior parietal cortex, the pulvinar nucleus in the thalamus and dorsolateral prefrontal cortex (see LaBerge, 1995 for an overview and model, and Hopfinger et al, 2000.) Default or resting state network A second example based on imaging studies has led to identification of a brain network activated not during performance of a cognitive tasks but in conditions of rest. This network labeled alternating as default mode, intrinsic connectivity or resting state network involved medial frontal cortex, posterior parietal cortex and the inferior parietal lobe (Greicius et al 2003; see figure 7 lower panel). Activity in the default mode network has been linked to core processes of spontaneous human cognition, like monitoring the environment, self awareness, wandering thoughts and emotions. These new findings are also reminiscent of William James’ famous sentences following his statement that ‘everyone knows what attention is’. James was not primarily interested in attention constrained by cognitive tasks. His central interest was the phenomenological side of attention: the wandering mind and ‘stream of consciousness’. 8

The above studies clearly illustrate how complex sub-domains of cognition like attention but also spontaneous thoughts involve global distributed networks composed of more localized specialized networks. In a similar vein, human memory is localized in the sense that ‘particular brain systems represent specific aspects of each event, and is distributed in the sense that many neural systems participate in representing the whole event’ (quote from Larry Squire, 1989). The overall implication of these studies was the insight that functional classifications derived from the cognitive domain are not just thin air, but a product of activity of the brain. Suggesting that mind and brain represent the two sides of the same coin, or stated differently that mental events are equivalent to certain brain events. The bridging of the conceptual gap between cognition and the brain in modern cognitive neuroscience implied the end of Cartesian dualism. It also meant that the ‘ghost in the machine’, a term introduced by the British philosopher Gilbert Ryle to criticize the concept of the mind as an entity separate from the body, was now definitely expelled from the brain to be replaced by hardwired neural networks.

References • • • • •

• • • • • • • • •

Arbib, M.A. et al. (1998). Neural organization. Structure, function and dynamics. Cambridge, PMIT Press. Baddeley, A.D. & Hitch, G.J. (1994). Developments in the concept of working memory. Neuropsycho-logy, 8, 485-493. Baars, B. (1988). A cognitive theory of Consciousness. Cambridge University Press. Cambridge Baars, B. (2003). The global brain web: An update on global workspace theory Guest editorial, Science and Consciousness Review. Barlow, H. (1994). What is the computational goal of the neocortex? In: C. Koch & J.L. Davis (Eds.), Large-scale Neuronal Theories of the Brain. (pp. 1-22). Cambridge: MIT Press. Barrett, H. C. & Kurzban, R. (2006) Modularity in cognition: framing the debate. Psychol. Rev. 113, 628–647. Bassett, D. S., et al. (2010). Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. U.S.A.108, 7641–7646. Buckner, R.L, Andrews-Hanna, J.S & Schacter, D.L (2008). The Brain’s Default Network Anatomy, Function, and Relevance to Disease. Ann. N.Y. Acad. Sci. 1124: 1–38 Churchland, P.S. & Sejnowski, T.S. (1992). The Computational Brain. Cambridge: MIT Press Coltheart, M. (1999) Modularity and cognition. Trends Cogn. Sci. 3, 115–120. Cowan, N. (1995). Attention and Memory. An integrated framework. New York: Oxford University Press. Damasio, A. (2010). Self comes to mind. Constructing the Conscious Brain. Pantheon Books. N.Y. Damasio, A.R. & Damasio, H. (1994). Cortical Systems for Retrieval of Concrete Knowledge: The Convergence Zone Framework. In: C. Koch & J.L. Davis (Eds.), LargeScale Neuronal Theories of the Brain. (pp. 61-74) Cambridge: MIT Press. Deen B, et al. (2017) Organization of high-level visual cortex in human infants. Nat. Commun. 8, 13995 9

•

• • • • •

• • •

• • • • • • • • • • • • • •

• • •

Dehaene, S., Sergent, C. & Changeux, J. P. 2003 A neuronal network model linking subjective reports and objective physiological data during conscious perception. Proc Natl Acad Sci U S A 100, 8520-5. Felleman, D.J. & van Essen, D.C. (1991). Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1, 1-47.Fodor, J. A. The Modularity of Mind: An Essay on Faculty Psychology (MIT Press, 1983). Fuster, J.M. (1995). Memory in the cerebral cortex. Cambridge: MIT press Georgopoulos, A.P. (1995). Motor cortex and cognitive processing. In: M.S. Gazzaniga (Ed.), The Cognitive Neurosciences (pp. 507-518). Cambridge: MIT Press. Friston, K.J. et al. (1993) Functional connectivity: the principal component anaylsis of large (PET) data sets. J. Cerb Blood Flow Metab, 13: 5-14 Gidde, B. and Claudia Voelcker-Rehage (2010) More Automation and Less Cognitive Control of Imagined Walking Movements in High- Versus Low-Fit Older Adults. Front Aging Neurosci. 2: 139. Gillihan, S. J., & Farah, M. J. (2005). Is self special? A critical review of evidence from experimental psychology and cognitive neuroscience. Psychological Bulletin, 131, 76–97. Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature 536, 171–17 Greicius, M.D., Krasnow, B., Reiss, A.L., and Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. USA 100, 253–258. Hebb D.O (1949). The Organization of Behavior. New York: Wiley and Sons Hofstadter, Douglas (2007). I Am A Strange Loop. Basic Books. Hopfinger, J. B., Buonocore, M. H., & Mangun, G. R. (2000).The neural mechanisms of topdown attentional control. Nature Neuroscience, 3, 284–291. Hopfinger, J.B., Woldorff, M.G., Fletcher E.M. & Mangun, G.R. (2001). Dissociating top-down attentional control from selective perception and action Kelly, A.M.C. & Garavan, H. (2005). Human functional neuroimaging of brain changes associated with practice. Cerebral Cortex, 15, 1089-1102. Koene, R. et al. (2009) NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies Neuroinformatics 7 (3), 195-210 Kok, A. (2016) Het Hiërarchisch Brein. Inleiding tot de Cognitieve Neurowetenschap. Pumbo. Koestler, Arthur (1967). The Ghost in the Machine (1990 reprint) Penguin Group. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking Press LaBerge D. (1995). Attentional Processing. Cambridge: Harvard University Press. Lashley K.S. (1929). Functional determination of cerebral localization. Archives of Neurology and Psychiatr, 38, 371-387 Mesulam, M. (1990). Large-Scale Neurocognitive Networks and Distributed Processing for Attention, Language and Memory. Annals of Neurology, 28 597-61. Mill, R. D., Ito, T., and Cole, M. W. (2017). From connectome to cognition: the search for mechanism in human functional brain networks. Neuroimage. Moscovitch, M., Goshen-Gottstein, Y. & Vriezen, E. (1994) Memory without conscious recollection: a tutorial review from a neuropsychological perspective. In: C. Umilta. & M. Moscovitch (Eds.), Conscious and Nonconscious Information Processing. Attention and Performance XV (pp. 619-660). Cambridge: MIT press Newman, M. E. J. (2006). Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103, 8577–8582. Poldrack, R. A. Can cognitive processes be inferred from neuroimaging data. Trends Cogn. Sci. 10, 59–63 (2006). Raichle, M. et al. (2001). A default mode of brain function. Proc. Natl. Acad. Sci. U.S.A., 98, 676–687 10

•

• • • • • • • • •

• •

Ramos-Nuñez, A.et al. Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance. Frontiers in Human Neuroscience, 2017; 11 Ryle, H. (2002). The Concept of Mind. University of Chicago.Press (Orginal edition in 1949). Schacter, D.L. (1987). Implicit and explicit memory. History and current status, J.Exp.Pscyhol.Learn. Mem.Cogn. Seeley et al. (2007). Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control J. Neurosci., February 28, 27(9):2349 –2356 Sporns O, Tononi G., Kötter R. (2005) The human connectome: A structural description of the human brain. PLOS Comput Biol 1:42. Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17, 652–660 (2014). Spunt, R.P & R.Adolphs (2017) A new look at domain specificity: insights from social neuroscience Nature Reviews Neuroscience 18, 559–567 Squire, L. (1989). Mechanisms of Memory. In: K.L. Kelner & D.E. Koshland. Molecules to Models: Advances in Neuroscience. In: S.M. Kosslyn & R.A. Anderson (Eds.), Frontiers in Cognitive Neuroscience (pp. 500-515). Cambridge: MIT Press. Sternberg, R. J. (1989) Domain-generality versus domain-specificity: the life and impending death of a false dichotomy. Merrill-Palmer Quarterly 35, 115–130 Swaab. D. (2011). Wij zijn ons brein. Uitgeverij Contact. Van den Heuvel M.P & Sporns O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, December 2013, Vol. 17, No. 12, 683-696 Van den Heuvel, M.P. (2009). The connected brain. Dissertation. UMC. Utrecht. Van den Hurk et al. (2017) Development of visual category selectivity in ventral visual cortex does not require visual experience PNAS, Published online May 15.

11