ABSTRACT whose dimensions are determined by the slice

ABSTRACTThe conventional fMRI paradigms have theirlimitation in spatial resolution in which they depend on the concept of subtractingthe activations between different attributes of stimuli by averaging the entireneural populations. With the introduction fMRI adaptation paradigms, a gradualreduction of BOLD signal caused by repeated presentations of stimulus, thestudy of selectivity of neuronal populations on a sub-voxel scale seemspossible. Not only do recent studies have provided evidence that the fMRIsignal could reveal neural selectivity under the fMRI adaptation paradigms, butalso enabled the investigation of the invariance of the neural responses fromthe neural populations within the imaged voxels which in turn enhance thefunctional resolution. Several mechanisms, including neural fatigue, mismatchin the observed and expected stimuli, sharpening model and facilitation model,have been proposed to account for the induced reduction in BOLD signal. However,there are still many unknowns for the underlying neural mechanisms of fMRIadaptation paradigm and further investigations should be considering on theexamination of the neural basis of the paradigm.

INTRODUCTIONFunctional magnetic resonance imaging (fMRI) hasbeen a useful tool as the gateway of human brain function in which it measuresthe blood oxygenation level-dependent (BOLD) signal related to the change of blood flow due to local neural activity. AlthoughfMRI offers a noninvasive measurement of brain activity that is secondary tothe electrical activity of neuronal firing withmoderate temporal resolution and good spatial resolution when compared withother neuroimaging techniques, the spatial resolution is limited to a millimeterscale due to its fundamental property of an indirect measure of neural activity(Fukuda, 2016). Spatial resolution of an fMRI study indicates the extent towhich it discriminates between nearby locations; and it is measured by the sizeof voxels, a three-dimensional rectangular cuboid, whose dimensions are determinedby the slice thickness, area of a slice and the grid imposed on the slice duringthe scanning process (Tsougos, 2017). A voxel typically contains many thousandsto possibly millions of neurons with various properties; therefore, attemptingto establish a direct selectivity of their responses is almost implausible.

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With the introduction of a new methodologicalapproach, fMRI adaptation, the study of selectivity of neuronal populations ona sub-voxel scale seems possible even in the absence of requiring a smaller voxelsize. The fMRI adaptation is a phenomenon in which repeated and prolonged presentationof the same visual stimulus causes a consistent and gradual reduction inactivation in a given voxel, and the reduction is selective to the particularcharacteristics of the repeated stimulus (Larsson, Solomon, & Kohn, 2016).The application of fMRI adaptation has been widely used in cognitiveneuroscience in recent years, typically in visual processing such as thestudies of direction of motion (Kar & Krekelberg, 2016), spatial processing(Zimmermann et al., 2016) and faces (Harris, Young, & Andrews, 2014), dueto several potential advantages from the implication of fMRI adaptation effect.For instance, the signal-to-noise ratio of an image is relatively low limitedby the spatial resolution of the technique itself and vascular processes includingcardiac activity and respiration unrelated to the neuronal function introduceunwanted signal or noise to the fMRI data in which fMRI adaptation helps to overcomesuch problem by identifying the regions that are selective to certain stimuli.

Asthe application of fMRI adaptation is most commonly observed in studies relatedto visual processing, there have been researchers proposing the relevancebetween fMRI adaptation and visual priming in which priming refers toimprovement in performance reflected by shortened reaction time and enhancedaccuracy with repeated presentations of stimuli on a behavioral aspect (Ganelet al., 2006; Xu, Turk-Browne, & Chun, 2007). Considering the similarity ofthe nature between fMRI adaptation and visual priming by repeated and prolongedpresentations of a stimulus, however, it would be counterintuitive that areduction in cortical response reflected by the decrease in BOLD signal iscorrelated with an improvement in behavioral performance. In an attempt to understand and discuss further onfMRI adaptation, the current review addresses particularly on the potentialadvantages that could be brought by the implication of fMRI adaptation effectand then followed by the underlying neuronal mechanisms that have beenproposed.  POTENTIAL ADVANTAGESAs mentioned earlier, the conventional fMRIparadigms have their limitation in spatial resolution where they rely on theconcept of subtraction of activation between different stimulus attributes byaveraging the entire neural populations which might give homogenous responsestowards stimulus changes or respond differentially tuned for different stimulustypes (Larsson, Solomon, & Kohn, 2016).

Therefore, it would be difficultand almost impossible to infer the underlying properties of the imaged neuralpopulations. With the introduction of the fMRI adaptation paradigm to recentstudies by repeated and prolonged presentations of stimuli, neuronalpopulations could be studied beyond the limitation in spatial resolution andthe fMRI adaptation paradigm serves as a tool to study the properties ofneuronal populations on a sub-voxel scale. In general, these paradigms capturethe reduction in neural responses when the stimulus has been presentedrepeatedly or for a prolonged time in which an increase in neural responseelicited by the change in particular stimulus attributes confirms the role ofneural populations that are tuned to a specific stimulus attributes beingmodified (Larsson & Smith, 2011). The fMRI paradigm has been adopted in arecent study to understand the feature-salience hierarchy in face processingwhere participants were asked to detect changes of faces presented repeatedlywith either slight modification or no modification to examine whether differentface feature contribute differentially to the neural signal in face responsiveregions such as the fusiform face area (Lai et al., 2014). Apart from the studiesconcerning visual processing which are commonly being introduced with thetechnique, other cognitive processing studies have also included such techniqueto study neural representations among the neural populations that are selectivefor specific stimulus properties.

For instance, a study has been conducted onvoice processing to test whether the neurons responding to human voice andmusical stimuli are distinct or overlapping (Armony et at., 2015). More recentstudies have also demonstrated the neural selectivity using fMRI adaptation inwhich certain neural populations are tuned to specific visual features, such ascolor and orientation. However, there has been debate about the degree of neuralselectivity demonstrated by the fMRI adaptation in early visual cortex andprevious studies have suggested that the conflicting results could be due tothe untuned neurons in V1 (Henry,2013).The above studies provide evidence that the fMRIsignal could reveal neural selectivity under the fMRI adaptation paradigms andindeed, such paradigms also allow the investigation of the invariance of theneural responses from the neural populations within the imaged voxels by taggingspecific neuronal populations within an area in the brain (Barron, Garvert,& Behrens, 2016). Limited by the spatial resolution and the enormouslylarge number of neurons in one voxel, it is unable to study the invariantproperties of cortical neurons by conventional fMRI paradigms.

In order tostudy the neural invariance to certain attributes, stimulus is presentedrepeatedly or with a prolonged time until the neuronal population is adaptedfollowed by the change of property of the stimulus. It is noted that stimulicould undergo only one transformation at a time to examine the invariance ofthe tagged neuronal populations to a specific attribute. The adapted fMRIsignal indicates the invariance of the tagged neurons to a particular attributewhile an increase in fMRI signal recovering from the adapted state implies thatthe tagged neurons are sensitive to the particular property being changed (Grill-Spector& Malach, 2001). Previous study has employed the fMRI adaptation paradigmto investigate the image-invariant face recognition across familiar andunfamiliar faces in face-selective regions by presenting either differentimages of the same identity or different image of different identities offamiliar and unfamiliar faces (Weibert, 2016).

Another research has alsodemonstrated the study of neuronal invariances on color specificity in the V4-complexunder the fMRI adaptation paradigm by varying the color of the prime and thecolor of the target (Van Leeuwen, 2014).The adaptation shown by a gradual reduction in BOLDsignal with repeated presentations across changes between stimuli demonstrates commonneural representation invariant to attributes being varied and the recoveryfrom adaptation shown by an increase in BOLD signal implies the selectivity ofspecific stimulus properties among neuronal representations.PROPOSED NEURONAL MECHANISMSFrom previous discussions, it is noted that thefMRI adaptation effect could be elicited across brain regions with differentperceptual modalities; thereby, it is expected that there would be variousmechanisms involved in the observed effect. Although there have been severalpotential advantages brought by the fMRI adaptation paradigm, the underlying neuralmechanisms are not fully understood. Several mechanisms have been proposed toaccount for the reduction in neural responses observed after prolonged presentationsof a stimulus, ranging from simple neural fatigue to complex interactionframeworks; and the suggested mechanisms will be discussed in the following. With repeated presentations of a stimulus,reduction of neural activity can be observed and it could be caused by areduction of firing rates of neurons due to neural fatigue. In a typical fMRIadaptation experiment, prolonged and repeated presentations of stimuli are eitheridentical or with the targeted attributes being varied that aim to produce anadaptation effect and recovery responses respectively. Given the similaritiesof perceptual adaptation experiments studying the habituation effect caused bystimuli repetitions, the reduction in BOLD signal under the fMRI adaptationparadigm could possibly be understood in terms of habituation which can bedescribed as a decrease in the strength of a response due to repeatedpresentations of a stimulus.

However, such conventional view of fMRI adaptationparadigm on the reflection of neural fatigue does not fully interpret and coverthe recovery responses after varied properties have been presented. Anotherproposed model has suggested the differences observed in identical and variedconditions as the mismatch in the observed and expected stimuli; and neuronalevidence has shown that, to certain extent, expectation in stimuli influencesthe fMRI adaptation regardless of the stimulus presentation duration butattention is critical to effects of expectation (Larsson & Smith, 2012). In fact, a reduction in BOLD signal after stimulirepetitions does not necessarily indicate neural fatigue or a decline inperformance. On the contrary, such reduction in neural activity under the fMRIadaptation paradigm may imply a sharpening and facilitation model. Thesharpening model suggests that the reduction in neural activity could possiblydue to an improved processing by inhibiting non-selective neurons that areinitially activated while the selective neurons in the neuronal populationsremains activated; therefore, the number of neurons involved in thecorresponding processing has decreased in order to offer an efficient and sparserepresentation across the cortex (Weiner et al., 2010). In other words, themodel explains the fMRI adaptation paradigm as the suppression of non-selectiveneurons that are irrelevant to the properties of the visual stimuli while theactivation state of the selective neurons remain unchanged. However, it is unclear whether fewerneurons involving would lead to a faster processing which corresponds to anenhancement in discrimination speed in visual priming.

Anotherpossible mechanism, the facilitation model, has suggested that information ofthe input may have stored on the selective neurons that are specifically responsiblefor the presented stimuli after the first encounter of processing. As a result,less neural processing is required to respond to the same stimulus withrepetitions and less processing time is expected. The reduction in neuralprocessing could be attributed to shortened neural duty cycle in which thestrength of neuron firing is increased but with a shorter duration (Kar &Krekelberg, 2016).Previous studies have shown several mechanismsunder the fMRI adaptation paradigms, such as the reduction in firing ratereflecting neural fatigue, sharpened neural responses with the inhabitation ofnon-selective neurons, facilitated processing attributed to shortened neuralduty cycle; however, there are still many unknowns for the neural mechanisms offMRI adaptation and probably other possible mechanism that could explain theadaptation effect. CONCLUSIONThe fMRI adaptation is a phenomenon whererepetitions of the same visual stimulus cause a consistent and gradualreduction in activation in a given voxel that serves as a tool to study theproperties of neuronal populations on a sub-voxel scale. Specifically, not onlydo recent studies have provided evidence that the fMRI signal recorded could revealneural selectivity under the fMRI adaptation paradigms, but also enabled theinvestigation of the invariance of the neural responses from the neuralpopulations within the imaged voxels enhancing the functional resolution. The adaptationeffect reflected by a gradual reduction in BOLD signal with repeatedpresentations across changes between stimuli demonstrates common neural populationsinvariant to attributes being varied and the recovery responses from adaptationshown by an increase in BOLD signal implies the selectivity of specificstimulus properties among neuronal representations.There are several mechanisms being proposed toaccount for the reduction in neural responses observed after a stimulus beingpresented repeatedly or with a prolonged time.

Neural fatigue is one of themost commonly proposed mechanisms that might induce the adaptation effect as aresult of a reduction in neuronal firing rate. Another model also suggests themismatch in the observed and expected stimuli could lead to the differences observedin identical and varied conditions. A reduction in BOLD signal does notnecessarily indicate neural fatigue or a decline in performance but asharpening and facilitation model.

The sharpening model explains the reductionin BOLD signal as the suppression of non-selective neurons that are irrelevantto the properties of the visual stimuli and therefore, less neurons areinvolved in the processing; while the facilitation model proposes that thereduction in BOLD signal could be due to the fact that information of the inputmight have primed on the selective neurons that are specifically responsiblefor the presented stimuli after the initial presentation of processing. Lessprocessing time and neural processing are required to respond to the samestimulus which could be attributed to shortened neural duty cycle. The abovementioned mechanisms that are responsible for inducing the adaptation effectmight aim to reduce cognitive and neural costs for irrelevant, knowninformation and allocate resources to the processing of relevant and newlyreceived information.  BIBLIOGRAPHYArmony,J. L., Aubé, W., Angulo-Perkins, A., Peretz, I.

, & Concha, L. (2015). Thespecificity of neural responses to musicand their relation to voice processing: An fMRI-adaptation       study. Neuroscience letters, 593,35-39.

Barron,H. C., Garvert, M. M., & Behrens, T. E. (2016). Repetition suppression: ameans to      index neural representationsusing BOLD?.

 Philosophical Transactions of the Royal       Society B: Biological Sciences, 371(1705).Fukuda,M., Poplawsky, A. J.

, & Kim, S. G. (2016). Submillimeter-resolution fMRI:Toward   understanding local neuralprocessing. Progress in brain research, 225, 123-152.Ganel,T., Gonzalez, C. L.

, Valyear, K. F., Culham, J.

C., Goodale, M. A.

, &Köhler, S. (2006).        The relationshipbetween fMRI adaptation and repetition priming. Neuroimage, 32(3),       1432-1440.Grill-Spector,K., & Malach, R.

(2001). fMR-adaptation: a tool for studying the functional      properties of human corticalneurons. Acta psychologica, 107(1), 293-321.Harris,R. J., Young, A.

W., & Andrews, T. J. (2014). Brain regions involved inprocessing        facial identity andexpression are differentially selective for surface and edge information. NeuroImage, 97,217-223.Henry,C.

A., Joshi, S., Xing, D., Shapley, R. M., & Hawken, M.

J. (2013).Functional      characterization of theextraclassical receptive field in macaque V1: contrast, orientation,      and temporal dynamics. Journal ofNeuroscience, 33(14), 6230-6242.Kar,K., & Krekelberg, B.

(2016). Testing the assumptions underlying fMRIadaptation using intracortical recordingsin area MT. Cortex, 80, 21-34.

Lai,J., Pancaroglu, R., Oruc, I., Barton, J. J., & Davies-Thompson, J. (2014).

Neuroanatomic   correlates of thefeature-salience hierarchy in face processing: an fMRI-adaptation       study. Neuropsychologia, 53,274-283.Larsson,J., & Smith, A. T.

(2012). fMRI repetition suppression: neuronal adaptationor    stimulus expectation?. CerebralCortex, 22(3), 567-576.Larsson,J.

, Solomon, S. G., & Kohn, A.

(2016). fMRI adaptation revisited. Cortex, 80,   154-160.

 Tsougos,I. (2017). Advanced MR Neuroimaging: From Theory to Clinical Practice.CRC         Press.VanLeeuwen, T.

(2014). Color Specificity in the Human V4 Complex–An fMRIRepetition    Suppression Study. In Advancedbrain neuroimaging topics in health and disease-methodsand applications. InTech.Weibert,K., Harris, R.

J., Mitchell, A., Byrne, H., Young, A. W., & Andrews, T. J.(2016).

An image-invariant neuralresponse to familiar faces in the human medial temporal lobe.   Cortex, 84, 34-42.Weiner,K. S., Sayres, R.

, Vinberg, J., & Grill-Spector, K. (2010). fMRI-adaptationand    category selectivity in humanventral temporal cortex: regional differences across time scales. Journal of neurophysiology, 103(6),3349-3365.Xu,Y., Turk-Browne, N. B.

, & Chun, M. M. (2007). Dissociating task performancefrom fMRI repetition attenuation inventral visual cortex.

 Journal ofNeuroscience, 27(22), 5981–5985.Zimmermann, E., Weidner, R., Abdollahi, R. O.,& Fink, G.

R. (2016). Spatiotopic adaptation         in visual areas. Journal of Neuroscience, 36(37),9526-9534. 

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