Effective connectivity in the neural network underlying coarse-to-fine categorization of visual scenes. A dynamic causal modeling study

According to current models of visual perception scenes are processed in terms of spatial frequencies following a predominantly coarse-to-fine processing sequence. Low spatial frequencies (LSF) reach high-order areas rapidly in order to activate plausible interpretations of the visual input. This triggers top-down facilitation that guides subsequent processing of high spatial frequencies (HSF) in lower-level areas such as the inferotemporal and occipital cortices. However, dynamic interactions underlying top-down influences on the occipital cortex have never been systematically investigated. The present fMRI study aimed to further explore the neural bases and effective connectivity underlying coarse-to-fine processing of scenes, particularly the role of the occipital cortex. We used sequences of six filtered scenes as stimuli depicting coarse-to-fine or fine-to-coarse processing of scenes. Participants performed a categorization task on these stimuli (indoor vs. outdoor). Firstly, we showed that coarse-to-fine (compared to fine-to-coarse) sequences elicited stronger activation in the inferior frontal gyrus (in the orbitofrontal cortex), the inferotemporal cortex (in the fusiform and parahippocampal gyri), and the occipital cortex (in the cuneus). Dynamic causal modeling (DCM) was then used to infer effective connectivity between these regions. DCM results revealed that coarse-to-fine processing resulted in increased connectivity from the occipital cortex to the inferior frontal gyrus and from the inferior frontal gyrus to the inferotemporal cortex. Critically, we also observed an increase in connectivity strength from the inferior frontal gyrus to the occipital cortex, suggesting that top-down influences from frontal areas may guide processing of incoming signals. The present results support current models of visual perception and refine them by emphasizing the role of the occipital cortex as a cortical site for feedback projections in the neural network underlying coarse-to-fine processing of scenes.

[1]  S. Thorpe,et al.  The orbitofrontal cortex: Neuronal activity in the behaving monkey , 2004, Experimental Brain Research.

[2]  Yuji Takeda,et al.  Time course of the integration of spatial frequency-based information in natural scenes , 2010, Vision Research.

[3]  E. Rolls,et al.  Novel visual stimuli activate a population of neurons in the primate orbitofrontal cortex , 2005, Neurobiology of Learning and Memory.

[4]  Salvatore Torrisi,et al.  Advancing understanding of affect labeling with dynamic causal modeling , 2013, NeuroImage.

[5]  Rainer Goebel,et al.  From Coarse to Fine? Spatial and Temporal Dynamics of Cortical Face Processing , 2010, Cerebral cortex.

[6]  Arthur P. Ginsburg,et al.  Spatial filtering and visual form perception. , 1986 .

[7]  M. Koivisto,et al.  Recurrent Processing in V1/V2 Contributes to Categorization of Natural Scenes , 2011, The Journal of Neuroscience.

[8]  Karl J. Friston,et al.  Ten simple rules for dynamic causal modeling , 2010, NeuroImage.

[9]  M. Bar The proactive brain: using analogies and associations to generate predictions , 2007, Trends in Cognitive Sciences.

[10]  Leslie G. Ungerleider,et al.  Distributed representation of objects in the human ventral visual pathway. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Y. Gutfreund,et al.  Saliency mapping in the optic tectum and its relationship to habituation , 2014, Front. Integr. Neurosci..

[12]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[13]  S. Hillyard,et al.  Involvement of striate and extrastriate visual cortical areas in spatial attention , 1999, Nature Neuroscience.

[14]  Andrea De Cesarei,et al.  Global and local vision in natural scene identification , 2011, Psychonomic bulletin & review.

[15]  E. DeYoe,et al.  Concurrent processing in the primate visual cortex. , 1995 .

[16]  R. Deichmann,et al.  Concurrent TMS-fMRI and Psychophysics Reveal Frontal Influences on Human Retinotopic Visual Cortex , 2006, Current Biology.

[17]  G. Pourtois,et al.  Top-down effects on early visual processing in humans: A predictive coding framework , 2011, Neuroscience & Biobehavioral Reviews.

[18]  Natalia Y. Bilenko,et al.  The “Parahippocampal Place Area” Responds Preferentially to High Spatial Frequencies in Humans and Monkeys , 2011, PLoS biology.

[19]  Nathalie Guyader,et al.  Image phase or amplitude? Rapid scene categorization is an amplitude-based process. , 2004, Comptes rendus biologies.

[20]  L. Kaufman,et al.  Handbook of Perception and Human Performance. Volume 2. Cognitive Processes and Performance , 1994 .

[21]  Poggio Gf Spatial properties of neurons in striate cortex of unanesthetized macaque monkey. , 1972 .

[22]  Nikolaus Weiskopf,et al.  Hemispheric Differences in Frontal and Parietal Influences on Human Occipital Cortex: Direct Confirmation with Concurrent TMS–fMRI , 2009, Journal of Cognitive Neuroscience.

[23]  J. Bullier Integrated model of visual processing , 2001, Brain Research Reviews.

[24]  William D. Penny,et al.  Comparing Dynamic Causal Models using AIC, BIC and Free Energy , 2012, NeuroImage.

[25]  Karl J. Friston,et al.  Behavioral/systems/cognitive Effective Connectivity during Processing of Facial Affect: Evidence for Multiple Parallel Pathways , 2022 .

[26]  C. Malsburg,et al.  The role of complex cells in object recognition , 2002, Vision Research.

[27]  Dwight J. Kravitz,et al.  The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.

[28]  J. Hegdé Time course of visual perception: Coarse-to-fine processing and beyond , 2008, Progress in Neurobiology.

[29]  K. Grill-Spector The neural basis of object perception , 2003, Current Opinion in Neurobiology.

[30]  Robert Sekuler,et al.  Learning to imitate novel motion sequences. , 2007, Journal of vision.

[31]  S. Rombouts,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[32]  Carole Peyrin,et al.  Hemispheric specialization for spatial frequency processing in the analysis of natural scenes , 2003, Brain and Cognition.

[33]  S Marrett,et al.  Local and global attention are mapped retinotopically in human occipital cortex. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[34]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[35]  Michel Dojat,et al.  Retinotopic and Lateralized Processing of Spatial Frequencies in Human Visual Cortex during Scene Categorization , 2013, Journal of Cognitive Neuroscience.

[36]  Russell A. Epstein,et al.  The Parahippocampal Place Area Recognition, Navigation, or Encoding? , 1999, Neuron.

[37]  Nathalie Guyader,et al.  The coarse-to-fine hypothesis revisited: Evidence from neuro-computational modeling , 2005, Brain and Cognition.

[38]  N. Guyader,et al.  Is Coarse-to-Fine Strategy Sensitive to Normal Aging? , 2012, PloS one.

[39]  Peter Zeidman,et al.  Identifying Abnormal Connectivity in Patients Using Dynamic Causal Modeling of fMRI Responses , 2010, Front. Syst. Neurosci..

[40]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[41]  Christophe Phillips,et al.  Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects , 2011, NeuroImage.

[42]  Cathy J. Price,et al.  Auditory–Motor Interactions for the Production of Native and Non-Native Speech , 2013, The Journal of Neuroscience.

[43]  Karl J. Friston,et al.  Deconstructing the Architecture of Dorsal and Ventral Attention Systems with Dynamic Causal Modeling , 2012, The Journal of Neuroscience.

[44]  Nathalie Guyader,et al.  Coarse-to-fine Categorization of Visual Scenes in Scene-selective Cortex , 2014, Journal of Cognitive Neuroscience.

[45]  J R Lishman,et al.  Temporal Integration of Spatially Filtered Visual Images , 1992, Perception.

[46]  Cathy J. Price,et al.  Inter- and Intrahemispheric Connectivity Differences When Reading Japanese Kanji and Hiragana , 2013, Cerebral cortex.

[47]  Erin M. Harley,et al.  Why is it easier to identify someone close than far away? , 2005, Psychonomic Bulletin & Review.

[48]  A. Hyvärinen,et al.  Spatial frequency tuning in human retinotopic visual areas. , 2008, Journal of vision.

[49]  Christoph M. Michel,et al.  The Neural Substrates and Timing of Top–Down Processes during Coarse-to-Fine Categorization of Visual Scenes: A Combined fMRI and ERP Study , 2010, Journal of Cognitive Neuroscience.

[50]  E. Rolls,et al.  Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[51]  M. Catani,et al.  A diffusion tensor imaging tractography atlas for virtual in vivo dissections , 2008, Cortex.

[52]  David J Heeger,et al.  Neural correlates of sustained spatial attention in human early visual cortex. , 2007, Journal of neurophysiology.

[53]  A. Oliva,et al.  From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition , 1994 .

[54]  Karl J. Friston,et al.  Bayesian model selection for group studies , 2009, NeuroImage.

[55]  Bradford Z. Mahon,et al.  A bimodal tuning curve for spatial frequency across left and right human orbital frontal cortex during object recognition. , 2014, Cerebral cortex.

[56]  C. Cavada,et al.  The anatomical connections of the macaque monkey orbitofrontal cortex. A review. , 2000, Cerebral cortex.

[57]  G. Glover Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[58]  Dwight J. Kravitz,et al.  A new neural framework for visuospatial processing , 2011, Nature Reviews Neuroscience.

[59]  Moshe Bar,et al.  Visual predictions in the orbitofrontal cortex rely on associative content. , 2014, Cerebral cortex.

[60]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[61]  M. Bar A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition , 2003, Journal of Cognitive Neuroscience.

[62]  D. Mumford,et al.  The role of the primary visual cortex in higher level vision , 1998, Vision Research.

[63]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[64]  Cathy J. Price,et al.  Lateralization is Predicted by Reduced Coupling from the Left to Right Prefrontal Cortex during Semantic Decisions on Written Words , 2010, Cerebral cortex.

[65]  Yuanzhen Li,et al.  Measuring visual clutter. , 2007, Journal of vision.

[66]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[67]  Karl J. Friston,et al.  Comparing dynamic causal models , 2004, NeuroImage.

[68]  Christoph M. Michel,et al.  Hemispheric specialization of human inferior temporal cortex during coarse-to-fine and fine-to-coarse analysis of natural visual scenes , 2005, NeuroImage.

[69]  Louise Kauffmann,et al.  The neural bases of spatial frequency processing during scene perception , 2014, Front. Integr. Neurosci..

[70]  Monica Baciu,et al.  Cerebral regions and hemispheric specialization for processing spatial frequencies during natural scene recognition. An event-related fMRI study , 2004, NeuroImage.

[71]  Derek K. Jones,et al.  Occipito-temporal connections in the human brain. , 2003, Brain : a journal of neurology.

[72]  J. Movshon,et al.  Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat's visual cortex. , 1978, The Journal of physiology.

[73]  P Girard,et al.  Feedback connections act on the early part of the responses in monkey visual cortex. , 2001, Journal of neurophysiology.

[74]  H. Barbas Connections underlying the synthesis of cognition, memory, and emotion in primate prefrontal cortices , 2000, Brain Research Bulletin.

[75]  John S. Duncan,et al.  Noninvasive in vivo demonstration of the connections of the human parahippocampal gyrus , 2004, NeuroImage.

[76]  M. Bar,et al.  Magnocellular Projections as the Trigger of Top-Down Facilitation in Recognition , 2007, The Journal of Neuroscience.

[77]  E. Rolls,et al.  The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology , 2004, Progress in Neurobiology.

[78]  A. Dale,et al.  From retinotopy to recognition: fMRI in human visual cortex , 1998, Trends in Cognitive Sciences.

[79]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[80]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[81]  Michael S C Thomas,et al.  Multiple Routes from Occipital to Temporal Cortices during Reading , 2011, The Journal of Neuroscience.

[82]  M. Sereno,et al.  Retinotopy and Attention in Human Occipital, Temporal, Parietal, and Frontal Cortex , 2008 .

[83]  Nancy Kanwisher,et al.  A cortical representation of the local visual environment , 1998, Nature.

[84]  Keiji Tanaka,et al.  Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.

[85]  Nathalie Guyader,et al.  Spatial frequency processing in scene-selective cortical regions , 2015, NeuroImage.

[86]  H. Barbas,et al.  Pathways for emotion: interactions of prefrontal and anterior temporal pathways in the amygdala of the rhesus monkey , 2002, Neuroscience.

[87]  Karl J. Friston,et al.  A Dynamic Causal Modeling Study on Category Effects: BottomUp or TopDown Mediation? , 2003, Journal of Cognitive Neuroscience.

[88]  D. Bub,et al.  Does face inversion change spatial frequency tuning? , 2010, Journal of experimental psychology. Human perception and performance.

[89]  H. Hughes,et al.  Global Precedence, Spatial Frequency Channels, and the Statistics of Natural Images , 1996, Journal of Cognitive Neuroscience.

[90]  Marcela Perrone-Bertolotti,et al.  Dynamic Causal Modeling of Spatiotemporal Integration of Phonological and Semantic Processes: An Electroencephalographic Study , 2012, The Journal of Neuroscience.

[91]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .