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Understanding the Brain as an Endogenously Active Mechanism

William Bechtel (bill@mechanism.ucsd.edu)Department of Philosophy, University of California, San Diego

La Jolla, CA 92093-0119 USA

Adele Abrahamsen (aabrahamsen@ucsd.edu)Center for Research in Language, University of California, San Diego

La Jolla, CA 92093 USA

Abstract

Although a reactive framework has long been dominant incognitive science and neuroscience, an alternative frameworkemphasizing dynamics and endogenous activity has recentlygained prominence. We review some of the evidence for en-dogenous activity and consider the implications not only forunderstanding cognition but also for accounts of explanationoffered by philosophers of science. Our recent characteriza-tion ofdynamic mechanistic explanation emphasizes the co-

ordination of accounts of mechanisms that identify parts andoperations with computational models of their activity. Thesecan, and should, be extended to incorporate attention tomechanisms that are not only active, but endogenously active.

Keywords: philosophy of science; mechanistic explanation;dynamics; endogenous brain activity, resting state fMRI,brain default network

Introduction

Observe a living organism, from a bacterium to a fellowhuman being, and you see an endogenously active system.Introspect and you will observe, as did William James, acontinual flow of thoughts. If pressed, most cognitive scien-

tists will acknowledge that neural systemsfrom individualneurons to the brain as a wholeexhibit endogenous activ-ity. That is, some of the activity is internally (Greek endo)produced (German gennan); the causes and control of thisactivity is inside the system rather than reactive to inputsfrom outside the system. But cognitive scientists tend todisregard this when designing studies. Those in psychologypresent discrete stimuli in structured tasks designed to per-mit statistical analysis of the behavioral effects of independ-ent variables. Those in neuroscience, following the traditionof Charles Scott Sherrington (1923), commonly treat thebrain as a reactive system in which sensory inputs initiateneural processing that results ultimately in motor responses.

They may stimulate specific neurons or provide sensoryinputs with specific properties so that recorded neural activ-ity can be analyzed in terms of responses to inputs. In bothfields, variations in activity that cannot be associated withan input are treated as random fluctuations (noise). There isno doubt that this reactive framework in psychology andneuroscience has been enormously productive in identifyingthe parts, operations, and organization of the mechanismsresponsible for cognition. It soon reaches its limits, though,in seeking accounts of the orchestrated functioning of thosecomponents: their dynamics and coordination in real time.

The investigation of endogenous activity, though less in-fluential, has historical roots nearly as deep as those of thereactive approach. It was promoted by Thomas GrahamBrown (1914), for example, who studied decerebrate anddeafferented cats in Sherringtons laboratory at Liverpoolfrom 1910 to 1913. He found that the isolated spinal cord,even when not receiving inputs, generates patterns of activ-ity comparable to those exhibited during motor behavior

elicited by stimuli. Browns emphasis on endogenous activ-ity initially was largely ignored (for discussion, see Stuart &Hultborn, 2008) but was revived several decades later whenbiologists recognized a class of neural circuitscentralpattern generatorswhose self-sustaining patterns of activ-ity generated rhythmic motor behavior even in the absenceof sensory input. After Wilson and Wyman (1965) pio-neered this construct in their account of locust flight, othersidentified central pattern generators in the brain stem andspinal cord for walking, swimming, respiration, circulation,and other behaviors for which oscillatory control was cru-cial (Grillner, 2003). Endogenous activity has received farless attention from those studying sensory processing andcentral cognition rather than motor control, despite indica-tions of endogenous oscillatory activity in cerebral cortexusing techniques ranging from single cell recording to EEGand fMRI. In the next section we describe highlights fromthis research and in the subsequent section briefly explorethe implications for reconstruing how we understand cogni-tive activity. Most important, if the conception of the brainas endogenously active is taken seriously, it profoundlychallenges the reactive perspective that has dominated muchof cognitive science as well as neuroscience: stimuli or tasksmust be regarded not as initiating activity in an inactive sys-tem, but rather as perturbing endogenous dynamic behavior.

The slow pace at which these fields are achieving achange of perspective is unsurprising considering the his-

tory of other sciences. Although Max Planck was exaggerat-ing when he said A new scientific truth does not triumphby convincing its opponents . . . but rather because its oppo-nents eventually die . . ., the considerable costs and uncer-tain benefits of change make it a tough sell. Uneven accep-tance of Einsteins revolutionary proposals is a familiar ex-ample. Less remarked upon is the delayed impact ofchanges in the sciences onphilosophy of science. For exam-ple, this young field (which did not even have a journal until1934) did not exhibit acute concern with the epistemologicalfoundations of science until it was confronted with Ein-

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steins proposals and their aftermatha response that nec-essarily involved at least a short delay. However, delays inuptake have been far greater for developments in sciencesother than physics, notably the biological and cognitive sci-ences. Philosophers of science did not even recognize thedominant mode of explanation in these sciencesmechanistic explanationuntil the 1990s and especiallyafter 2000. More recently, we have argued that such devel-opments as computational modeling of the dynamics ofcognitive and neural mechanisms require philosophers ofscience to extend their notion of mechanism to include dy-namic mechanistic explanation. In the last section of thispaper we will briefly characterize these two explanatoryframeworks and consider how the philosophical understand-ing of dynamic mechanistic explanation can incorporate theimplications of scientific work on endogenous activity.

Evidence that the Brain is Endogenously

Active

Although lesion and stimulation techniques have been im-portant in identifying brain regions involved in differentcognitive activities, since the mid-20th century the greatestinsights have come from techniques in which researchersrecord brain activity of individual neurons (single or multi-cell recording) or brain regions (EEG and fMRI). Mostcommonly these techniques have been employed within thereactive framework in which stimuli are presented or tasksare assigned, responses within the brain recorded, and theseresponses pooled for analysis to remove variability not as-sociated with the intervention.

Each of these techniques, though, also has been employedin ways that reveal endogenous brain activity. Notably,Rodolfo Llins employed intracellular recordings to identifysystematic variations in the conductance of calcium ionsacross neural membranes. He showed how the manner inwhich these conductances varied through time enabled neu-rons in the inferior olive, a brainstem nucleus, to function assingle-cell oscillators capable of self-sustained rhythmicfiring independent of synaptic input (Llins, 1988, p.1659). (For a review of evidence and models showing howthese intrinsic oscillations when combined with synapticprocesses can generate synchronous thalamocortical oscilla-tions, see Destexhe & Sejnowski, 2003.)

A second line of evidence for endogenous brain activity,consistent with that of single-cell recording, emerged fromearlier studies by Hans Berger (1929) pioneering the identi-

fication of distinctive waveforms in electroencephalograph(EEG) recordings of brain activity. When he presented nostimuli or task demands but simply had subjects sit awakewith their eyes closed, he obtained high-amplitude oscilla-tions between 8 and 12 Hz that he dubbed alpha waves.When subjects instead viewed a stimulus or solved a prob-lem, alpha waves were supplanted by lower-amplitude,higher-frequency beta waves (12-30 Hz). Soon thereafter itwas determined that the EGG signal captured, not actionpotentials, but rather synchronized sub-threshold electricalpotentials across a population of neurons. In the 1960s, the

development of digital EEG and of powerful statisticaltechniques for decomposing complex EEG signals intocomponent waveforms brought further discoveries; notably,very high-frequency (25-100 Hz) gamma waves wereprominent in addition to beta waves when people performedvarious cognitive tasks. Moreover, synchronized oscillationsat all of these frequencies were found in both active andpassive conditions, but at different amplitudes.

Thus, both single-cell recording and EEG studies haveprovided evidence for endogenous brain activity. In thispaper we will focus on yet another line of evidence offeredby recent work on resting-state fMRI. The BOLD (bloodoxygen level dependent) signal employed in fMRI researchregisters the oxygen concentrations in the brain within areasthat can be as small as 2 mm. Until recently fMRI researchfocused nearly exclusively on finding higher values in theBOLD signal when a task condition is compared to a controlor resting state condition.1 For example, semantic process-ing of words (task condition) would be contrasted to readingwords aloud (control condition) or to lying still in the scan-

ner with eyes closed (resting condition). The interest inneuroimaging during a resting state, rather than during taskperformance, developed from researchers occasional ob-servations that a number of brain areas routinely exhibitedless activity in task situations than in the resting state. Toexplore further these intriguing observations, Shulman et al.(1997) conducted a meta-analysis of studies in which a taskcondition was compared to a non-task condition in whichthe same stimulus was present. They found that the areascommonly less active in task situations included posteriorcingulate cortex (PCC), precuneus, inferior parietal cortex(IPC), left dorsal lateral prefrontal cortex (left DLPFC), anda medial frontal strip that continued through the inferior

anterior cingulate cortex (ACC), left inferior frontal cortex,and left inferior frontal gyrus to the right amygdala. Turningthe focus from the fact that these areas are less active duringtasks to the fact that they are more active in the absence oftask requirements, Raichle and his collaborators (Raichle etal., 2001) suggested that together these areas constitute adefault network.

A major advance in understanding the default network re-sulted from analyzing the temporal dynamics of the BOLDsignal. A pioneering dynamical analysis of fMRI data wasprovided by Biswal, Yetkin, Haughton, and Hyde (1995),who obtained BOLD signal values every 250 msec after ahand movement and identified spontaneous low frequency

1 In referring to resting states, the assumption is not that the sub-jects brain is resting, but that he or she is not engaged in a specifictask or responding to a specific stimulus. Often the subject is askedto fixate on a cross-hair or lie still in the scanner with eyes closedbut not asleep. Fluctuations in activity that can be linked to physio-logical activity (cardiac or respiratory activity) are eliminated fromthe data through linear regression. In a critique of this research,Morcom and Fletcher (2007) focused on the privileging of theresting state. The insights into the default network on which wefocus, however, do not rely on the resting state being privilegedbut simply as revealing ongoing activity in brain networks notemployed in cognitive tasks.

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(less than 0.1 Hz) fluctuations in sensorimotor cortex. Thesefluctuations were synchronized across the left and righthemispheres and with those in other motor areas, which wasinterpreted as evidence of functional connectivity among allthese areas. Accordingly, the approach is referred to asfunc-tional connectivity MRI(fcMRI).

Employing fcMRI, Greicius, Krasnow, Reiss, and Menon(2003) demonstrated that if they used the PCC as a seed forstatistical analysis, they could identify synchronized fluctua-tions in a large cluster of areas: medial prefrontal cortex(including inferior ACC and orbitofrontal cortex), leftDLPFC, inferior parietal cortex bilaterally, left inferolateraltemporal cortex, and left parahippocampal gyrus. Takinginstead the inferior ACC as the seed area, they found corre-lated fluctuations in the PCC, medial prefrontal cor-tex/orbital frontal cortex, the nucleus accumbens, and thehypothalamus/midbrain. Since these regions were virtuallythe same as those showing activity in Shulmans restingstate data, Greicius et al. construed this as evidence for acohesive, tonically active, default mode network (p. 256)

with two subnetworks.While the default network exhibits greater activity in the

resting state than in task conditions, the areas showinggreater activity in task conditions still generate a BOLDsignal in the resting state and one can find correlations inthe dynamics across these areas (synchronized oscillations).These synchronized oscillations are, however, out of phasewith those in the default network. Comparing the defaultnetwork with one that exhibited greater activation in an at-tention-demanding task (intraparietal sulcus, frontal eyefield, middle temporal region, supplementary motor areas,and the insula), Fox et al. (2005) described oscillations inthe two networks as anticorrelated, whereas oscillations for

different areas within each network were positively corre-lated. This shows that both the default network and the net-work involved in attention-demanding tasks are coordinat-ing their activities within themselves in the absence of ex-ternal stimulation or task demands.

Researchers subsequently identified additional networksusing this strategy. That is, a set of areas with correlateddynamics (synchronized oscillations) under resting stateconditions were posited to constitute a network, further evi-denced by negative correlations with other networks (e.g.,Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007,differentiate six anticorrelated networks). Fox and Raichle(2007) concluded: A consistent finding is that regions with

similar functionalitythat is, regions that are similarlymodulated by various task paradigmstend to be correlatedin their spontaneous BOLD activity.

Although the oscillations revealed in fMRI are of a muchlower frequency (< 0.1 Hz) than those usually reported inEEG (1-80 Hz), researchers have found ways to relate them.Mantini et al., for example, found that Each brain networkwas associated with a specific combination of EEGrhythms, a neurophysiological signature that constitutes abaseline for evaluating changes in oscillatory signals duringactive behavior (p. 13170). For example, the default net-

work showed positive correlations with amplitude in alphaand beta band oscillations while the attention network ex-hibited negative correlations in these frequency bands.These correlations may reflect systemic coherence in brainfunctioning. In the cortex of mammals, the amplitude(power density) of EEG oscillations has been found to beinversely proportional to their frequency (1/f). Even moreinteresting, the phase of lower-frequency oscillations seemsto modulate the amplitude of those at higher frequencies,which results in a nesting relation between the frequencybands. (Lakatos et al., 2005, refer to this as "oscillatory hi-erarchy hypothesis") In addition, oscillations at lower fre-quencies tend to synchronize over more widely distributedareas of the brain than those at higher frequencies (Buzski& Draguhn, 2004). Such coupling can be particularly impor-tant when the brain is perturbed by a stimulus, since amodulation in low-frequency oscillations can, throughphase-locking with higher-frequency oscillations, yieldrapid changes at those frequencies.

The Significance of Endogenous Brain Activityfor Understanding Cognition

One might acknowledge endogenous activity in variousbrain networks, but deny that it is of any cognitive signifi-cance. Perhaps it merely reflects basic metabolic activityand bears no implications for cognition. However, the factthat each network oscillates at a characteristic frequency,,rather than fluctuating randomly, suggests that endogenousactivity has implications for understanding brain activitygenerallyincluding activity during cognitive functioning.We briefly explore different ways in which endogenousactivity may be important for understanding the brain as asystem for cognition.

First, if a mechanism responds to a stimulus by increasingits activity, and that activity already is oscillating, responseto the stimulus will vary depending on the phase of the os-cillation when the stimulus arrives. This is true of individualneurons. If the membrane voltage of a neuron oscillatesendogenously in a range below zero mV, as the evidencedeveloped by Llins and others indicates, then it will requirestronger input to exceed the threshold for generating an ac-tion potential when it happens to be at its most negativephase. The same principle applies to populations of neuronswhose oscillations are synchronized. In a variety of tasks inwhich a stimulus evokes a behavioral response, it is knownthat the response correlates with the magnitude of the

BOLD signal. Fox, Snyder, Zacks, and Raichle (2005)therefore investigated whether these effects could be ex-plained by synchronized spontaneous fluctuations in neu-ronal activity detectable with fMRI. Subjects were in-structed to press a button with the right hand when a stimu-lus was detected, resulting in evoked activity in the leftsomatosensory cortex. The researchers hypothesized that theongoing spontaneous fluctuations in the right somatosensorycortex provided an accurate measure of the spontaneouscontribution to activity in the left somatosensory area ateach timestep and succeeded in showing that these sponta-

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neous fluctuations contributed significantly to the amplitudeof blood flow in the left somatosensory areas after eachstimulus. In fact, the task-related increased blood flow couldbe analyzed as a linear addition to the current amplitude ofthe spontaneous fluctuation. From this they inferred that theunderlying spontaneous fluctuations affected perception andbehavior. They supported this conclusion more directly in asubsequent study, in which they determined that spontane-ous fluctuations accounted for variability in the force withwhich subjects pressed the button (Fox, Snyder, Vincent, &Raichle, 2007). When subjects were instructed as to howforcefully they should press the button, the pattern of neu-ronal activity was very different than that which arose whenthey were not instructed, allowing the investigators to dis-count the possibility that what they took to be spontaneousvariability was in fact an evoked response. Thus, their studycan be taken as initial evidence that the variability in en-dogenous brain activity is one source of the variability inmeasures of cognitive activity.

Second, endogenous activity in the brains default net-

work is the most obvious candidate for the neural underpin-nings of mindwandering (Antrobus, Singer, Goldstein, &Fortgang, 1970). In one of the early fMRI studies using theresting state, Andreasen et al. (1995) queried subjects aboutwhat they were doing and elicited reports of being engagedin a mixture of freely wandering past recollection, futureplans, and other personal thoughts and experiences. Sincethese activities involve episodic memory, and episodicmemory tasks are among those which do not lead to loweractivity in the default network, Andreasen et al. and subse-quent researchers (e.g., Buckner & Carroll, 2007) have sug-gested that the default network is involved in recalling per-sonal experiences and anticipating future ones. Intriguingly,

Li, Yan, Bergquist, and Sinha (2007) correlated trials onwhich subjects failed to detect stop signals in behavioraltasks with increased activity in the default network, as onewould expect if that network were involved in a personthinking distracting thoughts about past and future experi-ences. One factor that renders problematic such a charac-terization of the activity of the default network is that theoscillatory behavior of the default network is maintained aswell in sleep (Fukunaga et al., 2006) and under anesthesia(Vincent et al., 2007), when presumably spontaneousthoughts are not occurring.

Third, endogenous brain activity might be crucial forbuilding and maintaining certain types of organization in the

nervous system required for cognitive activity. There isgrowing evidence that the brain exhibits small-world or-ganization (Watts & Strogratz, 1998) in which most connec-tions link neighboring neurons, creating clusters that cancollaborate in processing specific information, but a fewlong range connections enable overall coordination (Sporns& Zwi, 2004). There also is evidence that while most brainareas have connections to only a few other areas, some havea large number of connections, thereby constituting hubs.Such an architecture provides a highly efficient organizationfor information processing, and it is notable that the default

network itself exhibits a small-world architecture with hubs.An important question is how such organization might arise.Rubinov, Sporns, van Leeuwen, and Breakspear (2009) ad-vanced the intriguing possibility that oscillatory neurons,developing connections when synchronized, might self or-ganize into a small world network with hubs. In support ofthis proposal they described a model by Gong and vanLeeuwen (2004) that employs a logistic map activationfunction for individual units that endogenously exhibit cha-otic behavior. This enables the emergence of temporarypatterns of synchronized oscillations even in the absence ofexternal stimulation. A Hebbian learning procedure estab-lishes new connections between pairs of units whose activ-ity is synchronized and prunes those between unsynchro-nized units. Even when these networks begin with randomconnectivity, they develop clusters linked to each otherthrough hubs. However, in real brains the initial state al-ready involves local regions with interconnections and ex-perience further shapes the emerging organization such thatthe outcome is a highly correlated brain capable of main-

taining multiple anticorrelated networks. That is, the archi-tecture of the information processing system may be shapedby both endogenous and exogenous activity.

In this section we have considered three suggestions as tohow endogenous activity in the brain may contribute to itsfunctioning as a cognitive system. Although it is too early to

judge which will prove most fruitful, clearly the time fordismissing endogenous activity as mere noise has passed.

Endogenously Activity and Mechanistic

Explanation

The evidence for endogenous activity in brains presentschallenges not only to the ways in which cognitive scientistsunderstand cognitive activity but also to philosophers con-strual of the explanatory frameworks used in science. Wementioned above that these construals lag behind the sci-ences, often far more than necessary. Until recently, phi-losophical accounts of explanation focused primarily onlaws and construed explanation as the subsumption of phe-nomena to be explained under these laws. While such anapproach might work in physics, where there are many wellestablished laws, it does not characterize explanations in thelife sciences, where there are few laws but an abundance ofphenomena to be explained (Cummins, 2000). What form ofexplanation is appropriate? In the past 20 years a number ofphilosophers of science have finally paid attention to biolo-

gists and, following their lead, construed explanation as thecharacterization of the mechanism responsible for a phe-nomenon of interest (Bechtel & Richardson, 1993; Bechtel& Abrahamsen, 2005; Machamer, Darden, & Craver, 2000;Thagard, 2006).

Although there are minor differences among these variousaccounts of mechanistic explanation, they concur in constru-ing a mechanism as consisting of component parts, each ofwhich performs one or more operations. Each operationproduces change in another part that triggers or affects theoperation of that part, and so forth. Cognitive psychologists,

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traditionally have posited operations that transform, copy, ormove representations without localizing them in parts of thebrain. Cognitive neuroscientists (and growing numbers ofcognitive psychologists) emphasize localization and chooseoperations at the appropriate grain for their brain recordingtechnology (Bechtel, 2008).

Given the focus on specifying a mechanism to explain agiven phenomenon, it is natural to conceive of the mecha-nism as having a specific beginning condition and continu-ing its operations until its task is completed. This sequentialconception of mechanism is most clearly captured in thedefinition offered by Machamer, Darden, and Craver(2000): Mechanisms are entities [parts] and activities [op-erations] organized such that they are productive of regularchanges from start or set-up to finish or termination condi-tions. If the start or set up conditions involve a stimulus ortask originating from outside the mechanism, we arrive atthe construal of a mechanism not only as sequential but alsoas reactive.

This reactive conception of a mechanism accords well

with the accounts offered in many areas of biology and cog-nitive science, but it is not adequate to characterize endoge-nously active systems as discussed in the previous sections.A sequentially organized mechanism will not exhibit en-dogenous activity. A minimal first step towards a mecha-nism capable of endogenous activity retains the general se-quential conception of the overall functioning of the mecha-nism but allows operations that are viewed as later in thesequential order to feed back, either negatively or positively,on operations thought of as earlier. With even a single nega-tive feedback loop it is possible to generate oscillatory be-havior. It has long been known (Goodwin, 1965) that if theoperations are appropriately non-linear and the system is

open to sources of energy, these oscillations may be self-sustained and not dampen to a steady state over time. Thesame is true of mechanisms employing positive feedback orcyclic organization (see Bechtel & Abrahamsen, 2010).

Accommodating these organizational principles requiresdropping the sequential characterization of a mechanism andinstead coordinating accounts of parts and operations withaccounts of their dynamics. The conception of mechanismhence becomes more dynamic: A mechanism is a structureperforming a function in virtue of its component parts, com-ponent operations, and their organization. The orchestratedfunctioning of the mechanism, manifested in patterns ofchange over time in properties of its parts and opera-

tions, is responsible for one or more phenomena (Bechtel& Abrahamsen, in press). Accounts that utilize this concep-tion exemplify what we have recently called dynamicmechanistic explanation. Often such accounts incorporatecomputational modeling of the real-time dynamics producedby feedback loops and other forms of cyclic organization.Moreover, a dynamic conception of mechanism and mecha-nistic explanation is compatible with the non-sequentialorganization, non-linear interactions, and openness to en-ergy required for endogenous operation.

A self-sustaining oscillatory mechanism can account forthe endogenous activity found in the brain, but now newexplanatory tasks arise. First, the phenomenon of interest istypically not generated by a single oscillatory mechanismbut by the coordinated behavior of multiple oscillators.Since Huygens we have known that if a signal can be passedbetween oscillators, they can synchronize their oscillations.However, depending on the particular ways in which oscil-lators are organized into a system, a population of oscilla-tors can come to exhibit extremely complex behavior. Sec-ond, even a single oscillator can be perturbed by externalinputs and the resulting change in its functioning can becomplex. Complexity is even greater when a population ofoscillators already exhibiting complex behavior is per-turbed. These are the sorts of challenges faced in under-standing how the brain, viewed as an endogenously activesystem, is presented with stimuli or tasks. Philosophicalaccounts of explanation must also reflect these challengesconfronted in neuroscience and cognitive science.

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