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• W3033588700 abstract "•Medial parietal cortex (MPC) has category-selective regions for faces and scenes•MPC subregions prefer recognizable examples of their selective stimuli•MPC shows category-selective interactions with medial temporal lobe (MTL)•MPC and MTL show coincident activity for category-specific identification The rapid recognition and memory of faces and scenes implies the engagement of category-specific computational hubs in the ventral visual stream with the distributed cortical memory network. To better understand how recognition and identification occur in humans, we performed direct intracranial recordings, in a large cohort of patients (n = 50), from the medial parietal cortex (MPC) and the medial temporal lobe (MTL), structures known to be engaged during face and scene identification. We discovered that the MPC is topologically tuned to face and scene recognition, with clusters in MPC performing scene recognition bilaterally and face recognition in right subparietal sulcus. The MTL displayed a selectivity gradient with anterior, entorhinal cortex showing face selectivity and posterior parahippocampal regions showing scene selectivity. In both MPC and MTL, stimulus-specific identifiable exemplars led to greater activity in these cortical patches. These two regions work in concert for recognition of faces and scenes. Feature selectivity and identity-sensitive activity in the two regions was coincident, and they exhibited theta-phase locking during face and scene recognition. These findings together provide clear evidence for a specific role of subregions in the MPC for the recognition of unique entities. The rapid recognition and memory of faces and scenes implies the engagement of category-specific computational hubs in the ventral visual stream with the distributed cortical memory network. To better understand how recognition and identification occur in humans, we performed direct intracranial recordings, in a large cohort of patients (n = 50), from the medial parietal cortex (MPC) and the medial temporal lobe (MTL), structures known to be engaged during face and scene identification. We discovered that the MPC is topologically tuned to face and scene recognition, with clusters in MPC performing scene recognition bilaterally and face recognition in right subparietal sulcus. The MTL displayed a selectivity gradient with anterior, entorhinal cortex showing face selectivity and posterior parahippocampal regions showing scene selectivity. In both MPC and MTL, stimulus-specific identifiable exemplars led to greater activity in these cortical patches. These two regions work in concert for recognition of faces and scenes. Feature selectivity and identity-sensitive activity in the two regions was coincident, and they exhibited theta-phase locking during face and scene recognition. These findings together provide clear evidence for a specific role of subregions in the MPC for the recognition of unique entities. The ability to identify previously encountered people and objects requires an interplay of activity between sensory and memory regions of the brain. To identify a unique object requires recognizing that it has been encountered before and to be able to retrieve information about it [1Yonelinas A.P. The nature of recollection and familiarity: a review of 30 years of research.J. Mem. Lang. 2002; 46: 441-517Crossref Scopus (2546) Google Scholar, 2Bird C.M. The role of the hippocampus in recognition memory.Cortex. 2017; 93: 155-165Crossref PubMed Scopus (40) Google Scholar, 3Jacoby L.L. Toth J.P. Yonelinas A.P. Separating conscious and unconscious influences of memory: measuring recollection.J. Exp. Psychol. Gen. 1993; 122: 139-154Crossref Scopus (615) Google Scholar, 4Mandler G. Recognizing: the judgment of previous occurrence.Psychol. Rev. 1980; 87: 252-271Crossref Scopus (1999) Google Scholar]. Traditionally, the hippocampus and parahippocampal gyri are implicated as the principal loci subserving these processes [1Yonelinas A.P. The nature of recollection and familiarity: a review of 30 years of research.J. Mem. Lang. 2002; 46: 441-517Crossref Scopus (2546) Google Scholar, 5Schoemaker D. Mascret C. Collins D.L. Yu E. Gauthier S. Pruessner J.C. Recollection and familiarity in aging individuals: Gaining insight into relationships with medial temporal lobe structural integrity.Hippocampus. 2017; 27: 692-701Crossref PubMed Scopus (4) Google Scholar, 6Schoemaker D. Gauthier S. Pruessner J.C. Recollection and familiarity in aging individuals with mild cognitive impairment and Alzheimer’s disease: a literature review.Neuropsychol. Rev. 2014; 24: 313-331Crossref PubMed Scopus (38) Google Scholar, 7Brambati S.M. Benoit S. Monetta L. Belleville S. Joubert S. The role of the left anterior temporal lobe in the semantic processing of famous faces.Neuroimage. 2010; 53: 674-681Crossref PubMed Scopus (44) Google Scholar, 8Martin C.B. McLean D.A. O’Neil E.B. Köhler S. Distinct familiarity-based response patterns for faces and buildings in perirhinal and parahippocampal cortex.J. Neurosci. 2013; 33: 10915-10923Crossref PubMed Scopus (38) Google Scholar]. Distinctive entities, such as faces and scenes, rely upon specific, category-selective constituents of the ventral visual stream and medial temporal lobe (MTL) for recognition. In recent years, it has become clear that the extended memory network underpinning recognition extends beyond the MTL to other regions, such as medial parietal cortex (MPC) [9Cabeza R. St Jacques P. Functional neuroimaging of autobiographical memory.Trends Cogn. Sci. 2007; 11: 219-227Abstract Full Text Full Text PDF PubMed Scopus (441) Google Scholar, 10Gardini S. Cornoldi C. De Beni R. Venneri A. Left mediotemporal structures mediate the retrieval of episodic autobiographical mental images.Neuroimage. 2006; 30: 645-655Crossref PubMed Scopus (73) Google Scholar, 11Valenstein E. Bowers D. Verfaellie M. Heilman K.M. Day A. Watson R.T. Retrosplenial amnesia.Brain. 1987; 110: 1631-1646Crossref PubMed Scopus (427) Google Scholar, 12Ranganath C. Ritchey M. Two cortical systems for memory-guided behaviour.Nat. Rev. Neurosci. 2012; 13: 713-726Crossref PubMed Scopus (639) Google Scholar, 13Argyropoulos G.P. Loane C. Roca-Fernandez A. Lage-Martinez C. Gurau O. Irani S.R. Butler C.R. Network-wide abnormalities explain memory variability in hippocampal amnesia.eLife. 2019; 8: e46156Crossref PubMed Scopus (14) Google Scholar, 14Gilmore A.W.W. Nelson S.M.M. McDermott K.B.B. A parietal memory network revealed by multiple MRI methods.Trends Cogn. Sci. 2015; 19: 534-543Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar]. Prior functional neuro-imaging studies have shown that the MPC is engaged in processing complex scenes [12Ranganath C. Ritchey M. Two cortical systems for memory-guided behaviour.Nat. Rev. Neurosci. 2012; 13: 713-726Crossref PubMed Scopus (639) Google Scholar, 15Silson E.H. Steel A.D. Baker C.I. Scene-selectivity and retinotopy in medial parietal cortex.Front. Hum. Neurosci. 2016; 10: 412Crossref PubMed Scopus (38) Google Scholar], path integration [16Knierim J.J. The hippocampus.Curr. Biol. 2015; 25: R1116-R1121Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar, 17Chrastil E.R. Sherrill K.R. Hasselmo M.E. Stern C.E. There and back again: hippocampus and retrosplenial cortex track homing distance during human path integration.J. Neurosci. 2015; 35: 15442-15452Crossref PubMed Scopus (57) Google Scholar, 18Hashimoto R. Tanaka Y. Nakano I. Heading disorientation: a new test and a possible underlying mechanism.Eur. Neurol. 2010; 63: 87-93Crossref PubMed Scopus (36) Google Scholar], spatial memory of landmarks [19Auger S.D. Mullally S.L. Maguire E.A. Retrosplenial cortex codes for permanent landmarks.PLoS ONE. 2012; 7: e43620Crossref PubMed Scopus (115) Google Scholar, 20Epstein R.A. Higgins J.S. Jablonski K. Feiler A.M. Visual scene processing in familiar and unfamiliar environments.J. Neurophysiol. 2007; 97: 3670-3683Crossref PubMed Scopus (102) Google Scholar], egocentric to allocentric transformation during navigation [21Zhang H. Copara M. Ekstrom A.D. Differential recruitment of brain networks following route and cartographic map learning of spatial environments.PLoS ONE. 2012; 7: e44886Crossref PubMed Scopus (32) Google Scholar, 22Dhindsa K. Drobinin V. King J. Hall G.B. Burgess N. Becker S. Examining the role of the temporo-parietal network in memory, imagery, and viewpoint transformations.Front. Hum. Neurosci. 2014; 8: 709Crossref PubMed Scopus (33) Google Scholar], and spatial imagery. It has also been shown to be active in assessments of face familiarity [23Bernard F.A. Bullmore E.T. Graham K.S. Thompson S.A. Hodges J.R. Fletcher P.C. The hippocampal region is involved in successful recognition of both remote and recent famous faces.Neuroimage. 2004; 22: 1704-1714Crossref PubMed Scopus (76) Google Scholar, 24Lee T.M.C. Leung M.K. Lee T.M.Y. Raine A. Chan C.C.H. I want to lie about not knowing you, but my precuneus refuses to cooperate.Sci. Rep. 2013; 3: 1636Crossref PubMed Scopus (15) Google Scholar, 25Gobbini M.I. Haxby J.V. Neural response to the visual familiarity of faces.Brain Res. Bull. 2006; 71: 76-82Crossref PubMed Scopus (132) Google Scholar, 26Visconti di Oleggio Castello M. Halchenko Y.O. Guntupalli J.S. Gors J.D. Gobbini M.I. The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception.Sci. Rep. 2017; 7: 12237Crossref PubMed Scopus (38) Google Scholar, 27Silson E.H. Steel A. Kidder A. Gilmore A.W. Baker C.I. Distinct subdivisions of human medial parietal cortex support recollection of people and places.eLife. 2019; 8 (e47391)Crossref PubMed Scopus (23) Google Scholar]. This engagement of MPC in both spatial and facial processing brings up obvious questions regarding its functional organization and its role in recognition. There is some evidence that the MPC engages with known areas of category selectivity within ventral visual pathways [28Kadipasaoglu C.M. Conner C.R. Whaley M.L. Baboyan V.G. Tandon N. Category-selectivity in human visual cortex follows cortical topology: a grouped icEEG study.PLoS ONE. 2016; 11: e0157109Crossref PubMed Scopus (20) Google Scholar, 29Ghuman A.S. Brunet N.M. Li Y. Konecky R.O. Pyles J.A. Walls S.A. Destefino V. Wang W. Richardson R.M. Dynamic encoding of face information in the human fusiform gyrus.Nat. Commun. 2014; 5: 5672Crossref PubMed Scopus (68) Google Scholar, 30Tang H. Buia C. Madhavan R. Crone N.E. Madsen J.R. Anderson W.S. Kreiman G. Spatiotemporal dynamics underlying object completion in human ventral visual cortex.Neuron. 2014; 83: 736-748Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar, 31Gomez J. Barnett M. Grill-Spector K. Extensive childhood experience with Pokémon suggests eccentricity drives organization of visual cortex.Nat. Hum. Behav. 2019; 3: 611-624Crossref PubMed Scopus (20) Google Scholar, 32Aguirre G.K. Zarahn E. D’Esposito M. An area within human ventral cortex sensitive to “building” stimuli: evidence and implications.Neuron. 1998; 21: 373-383Abstract Full Text Full Text PDF PubMed Scopus (369) Google Scholar, 33Kanwisher N. McDermott J. Chun M.M. The fusiform face area: a module in human extrastriate cortex specialized for face perception.J. Neurosci. 1997; 17: 4302-4311Crossref PubMed Scopus (127) Google Scholar] and the MTL [8Martin C.B. McLean D.A. O’Neil E.B. Köhler S. Distinct familiarity-based response patterns for faces and buildings in perirhinal and parahippocampal cortex.J. Neurosci. 2013; 33: 10915-10923Crossref PubMed Scopus (38) Google Scholar, 34Diana R.A. Yonelinas A.P. Ranganath C. High-resolution multi-voxel pattern analysis of category selectivity in the medial temporal lobes.Hippocampus. 2008; 18: 536-541Crossref PubMed Scopus (77) Google Scholar, 35Fairhall S.L. Anzellotti S. Ubaldi S. Caramazza A. Person- and place-selective neural substrates for entity-specific semantic access.Cereb. Cortex. 2014; 24: 1687-1696Crossref PubMed Scopus (30) Google Scholar, 36O’Craven K.M. Kanwisher N. Mental imagery of faces and places activates corresponding stiimulus-specific brain regions.J. Cogn. Neurosci. 2000; 12: 1013-1023Crossref PubMed Scopus (664) Google Scholar, 37Epstein R. Kanwisher N. A cortical representation of the local visual environment.Nature. 1998; 392: 598-601Crossref PubMed Scopus (2017) Google Scholar], with known scene and face areas differentially connected to different regions of MPC [27Silson E.H. Steel A. Kidder A. Gilmore A.W. Baker C.I. Distinct subdivisions of human medial parietal cortex support recollection of people and places.eLife. 2019; 8 (e47391)Crossref PubMed Scopus (23) Google Scholar]. Given the role that the MPC is thought to serve as a hub in memory processing, could it also play a role in coordinating category-specific memories alongside the ventral stream? These questions have been unanswerable until the current time, owing to the relative inaccessibility of the medial parietal lobe to time-resolved, electrophysiology (M/EEG) and the infrequency of focal lesions impacting this region [38Cavanna A.E. Trimble M.R. The precuneus: a review of its functional anatomy and behavioural correlates.Brain. 2006; 129: 564-583Crossref PubMed Scopus (3110) Google Scholar]. To resolve these questions, we performed intracranial recordings in a large human cohort during face- and scene-identification tasks, using both surface and penetrating electrode arrays. Spatially precise and highly temporally resolved recordings across a broad region were used to evaluate whether MPC is tuned to faces and scenes and, if so, its role in recognition and identification relative to the ventral visual stream and the MTL. Broadband gamma activity (BGA) (70–150 Hz) was recorded using intracranial electrodes in 66 participants (30 male, 18–56 years; 13 left handed) undergoing intracranial electrode placement for the localization of intractable epilepsy—18 patients had subdural grid electrodes (SDEs) and 48 had depth recordings using stereotactic EEG electrodes (sEEGs). Participants viewed visual images of famous faces, famous landmarks (scenes), or scrambled images and were asked to name the person or landmark (Figure 1). Mean identification accuracy (±SD) was 34% ± 19% for faces and 51% ± 20% for scenes. 16 patients who identified less than 20 face or scene stimuli were excluded from further analysis. Data from the remaining 50 participants, who identified faces and scenes with an overall accuracy of 39% ± 17% for faces and 56% ± 16% for scenes, were used for further analysis. Articulation latencies for this cohort were faces 1,502 ± 346 ms and scenes 1,479 ± 321 ms. Data from two representative patients are shown in Figure 2, each with electrodes in multiple locations within MPC. Each displayed loci with greater activation for one stimulus class over all other categories. TA603 had an SDE grid spanning the extent of the entire right MPC (Figure 2A). Three electrodes, all located over the subparietal sulcus [40Ono M. Kubik S. Abernathey C. Atlas of the Cerebral Sulci. Thieme Medical Publishers, 1990Google Scholar], showed prominent BGA increase (>200% above baseline) for faces, with smaller responses (<50%) to other stimulus classes (Figure 2C). Electrode X15, located more posteriorly, responded to both faces and scenes over scrambled stimuli but was not significantly selective to either category. No other electrodes were significantly responsive. A trial-by-trial analysis of activation (Figure 2D) showed a consistent onset time of activation relative to stimulus onset in face-selective electrodes that was not strongly coupled to response latency. TS106 had sEEG electrodes in medial parietal and occipital cortex (Figure 2B). The right subparietal sulcus in this case also showed prominent selectivity for face stimuli. Electrodes located in the parieto-occipital fissure were more active for scene stimuli and, in this case, were scene selective. This separation of preferential activation suggests that MPC processes faces and scenes via spatially distinct neural populations rather than a single, generic cognitive process. All 236 electrodes (99 LH; 137 RH), in 39 patients, located within MPC were evaluated for responsivity and selectivity to faces and scenes (Figures 3 and S1; Videos S1 and S2) in the 500- to 1,000-ms window. 142 (60%) were responsive, showing significantly greater activation to either faces or scenes than their scrambled versions. Of these, 21 (15%) were face selective, 60 (42%) were scene selective, and the remaining 61 (43%) showed no significant differences between these conditions (Figure 3A). There was no significant correlation between the amplitude of face-scene differences and grayscale-color scrambled differences (r = −0.10; p = 0.24; Figure S2). Face-responsive electrodes showed significantly greater activity for whole faces over face parts (Figure S3). In both hemispheres, there were a substantial number of electrodes with preferential activation to faces or scenes. Thus, faces and scenes appear to be processed via distinct and only partially overlapping neural substrates in MPC. eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiIwOWUxNjhiMDJkNDhiNjEwNDRmZTQ3ZDM0MWZiYjYwOCIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjM1NjE3Nzc4fQ.KRIfhYvJKASk3a7zdBrQGDIHnHup4sM3WpW_VYyLesNB24OvVUMQKCXXRMggUK_BxdUZGhZQ7Q75RJu07amM6FRiwICdDrp6O2j8Cs7b9Yg_ASvuGH64fdpNBn6kguWljniPt-eUBDvr5YkV2gfWs3HTBgUFpLfAJG74p4Mx9NAlwT6A-XzKGvHQ9Hj-gPMrto-9bE6g2hXmz-N3gFw2B78srzZQZu2np4lIEnkixkbEHESFdcxOMsUQdx7CGNdd5vKStmIDPt31hWLdi1fLE-5_TKpsONm3Nz5ucege4kLBCFRJ7WKJCK5A7Tr1kNOcnvIbb0RT8NE57XgaiGI6sA Download .mp4 (0.57 MB) Help with .mp4 files Video S1Spread of Face-Evoked Activity across the Cortical Surface, Related to Figure 3 Contrast MEMA video of the time course of broadband gamma activation across the cortical surface, displaying areas more active when viewing faces than scrambled faces. Regions in black did not have consistent coverage for reliable MEMA results. eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiIxYTUxZTc4ZDkzMjYxNDNkNzhjOWFlNDhjMzgzYjc3YSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjM1NjE3Nzc4fQ.bd8GwNyqQ1EqI-O_YpjS60xowknGjNZGLPD7WHyJA6dVEDySQr5-e2fcU9K-dA8Dq71GhXRptfS_voXob_Y6S3odfYbaSuF0NtaNRBw80Z0EE58RMf2vIMh5wFRauJaoHaYXHNtwD8HWaZzWTxCZevxF8qoJruBCbZBr_YI4nFYjXD0lWcq2pR8fvIevc34KQSdGr9EcAxxl96MQD_Xj5Xl3edDRsWsjcHHraxx8pIlNln1e5uXBVOfnUE7fsaxdsyOehv5xQhXg5pgcCpJ7lhrRRwKfaELpcYlX6v18CRR-QE8ADoVMnnW5kIdZwbOTtb3ad81crdLOkGH39d0Rjw Download .mp4 (0.71 MB) Help with .mp4 files Video S2Spread of Scene-Evoked Activity across the Cortical Surface, Related to Figure 3 Contrast MEMA video of the time course of broadband gamma activation across the cortical surface, displaying areas more active when viewing scenes than scrambled scenes. Regions in black did not have consistent coverage for reliable MEMA results. To further investigate this spatial separation, we used a mixed-effects multilevel analysis (MEMA) [41Kadipasaoglu C.M. Baboyan V.G. Conner C.R. Chen G. Saad Z.S. Tandon N. Surface-based mixed effects multilevel analysis of grouped human electrocorticography.Neuroimage. 2014; 101: 215-224Crossref PubMed Scopus (23) Google Scholar]. This method creates an effect size and precision estimate on the cortical surface modeling each electrode’s “recording zone” as an exponentially decaying geodesic radius. A mixed-effects analysis of the population is then performed at each node on the cortical surface. This method accounts for sparse sampling, outlier inferences, and intra- and inter-subject variability to create a population-level, surface-based representation. This analysis also showed bilateral scene-selective cortical clusters superior to the parieto-occipital fissure and in posterior precuneus. In the right hemisphere, a well-defined, face-selective cluster was seen, anatomically localized to the subparietal sulcus in the right hemisphere, with a small scene-selective cluster superior to it (Figure 3C; Video S3). eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiIwNGQyNDU3NjMyNGJlYjdiMjE3MzUzZDE5ODI0MmM3NSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjM1NjE3Nzc4fQ.c0mckA6eVKbejrAWt2lnD1t7T50eTL5RzuLIQsslrqSjRU6vOrKjAKgWsoeEyZtVQRDimrPyRQ0j2UbRvMSXrtT9xhyM_T66uF6ivFhr3AKCjgPPjPTGkaKpODdvxnH2i7idPLu5N6Dhsud4Ve3P31xyDVCi9g1iwTQq9ebOB3ctjLJ-qOyAHNSW8UtbUEnwVIWslABwaqjHVTjx5GVvHwwnM3wXgCOA2lJ8fy9tDluUxKwr_FCqnzH7qa3N1GIjljr86xaqsMsojY_Cy23-1glHnNQXay1li_d5YZCuqOj3qBPT6AZuNZUquhrdmwDYm34yTJzysJ0FY4lZmhnGfw Download .mp4 (0.6 MB) Help with .mp4 files Video S3Temporal Evolution of Category Selectivity across the Cortical Surface, Related to Figure 3 4D MEMA video of the contrast between the activations to faces and scenes across the cortical surface. Blue regions show preferential activation to faces and red represents preferential scene activation. Regions in black did not have consistent coverage for reliable MEMA results. To evaluate the sensitivity of cortical patches in the MPC to the patients’ abilities to identity the faces, we compared activation for correctly versus incorrectly answered face-naming trials within face-selective electrodes and within scene-selective electrodes with a significant face response. Of the 21 face-selective electrodes, 15 (71%) were face identification sensitive—they showed greater responses for correctly identified faces over incorrectly identified ones (Figure 3D). By contrast, 10 (36%) of the 28 scene-selective electrodes with significant face responsiveness showed face identification sensitivity. Of the 60 scene-selective electrodes, 22 (37%) were scene identification sensitive (Figure 3F) and only 5 (24%) of the 21 face-selective electrodes showed scene identification sensitivity. A multiple linear regression (r2 = 0.23), utilizing all responsive MPC electrodes, revealed that face identity sensitivity was associated with face selectivity (β = 0.94; p < 10−8) and scene identity sensitivity was associated with greater place selectivity (β = −0.28; p = 0.046). This means face- and scene-selective areas in MPC show preferential activation for known, identifiable exemplars of stimuli they are tuned to. An important question these findings raise is whether this response pattern of selectivity for faces versus scenes is present in other regions of the cortical memory network. If so, which region shows this phenomenon first? We applied identical analyses to characterize category selectivity and identification sensitivity in the MTL. Of 347 MTL electrodes (192 LH; 155 RH) in 42 patients, 216 (62%) were responsive. Of these, 21 (10%) were face selective, 59 (27%) were scene selective, and the remaining 136 (63%) were unselective (Figure 3B). The ratio of face- to scene-selective electrodes was not significantly different from that in MPC (χ2 [1; N = 161] = 0.002; p = 0.96). An anterior-posterior separation between face and scene selectivity was noted, with anterior, rhinal areas more likely to show face selectivity and posterior regions showing greater scene selectivity (r = 0.56; p < 10−5). As in MPC, there was no significant correlation between face-scene and grayscale-color differences (r = −0.11; p = 0.12; Figure S2), and face-responsive electrodes showed significantly greater activity for whole faces over face parts (Figure S3). At the population level, parahippocampal place area was discernable in the right hemisphere (Figure 3C), consistent with previous studies of MTL and ventral visual category selectivity (Figure S4). Of the 21 face-selective electrodes, 10 (48%) showed significant face identification sensitivity (Figure 3E). By contrast, 8 (24%) of 34 scene-selective, face-responsive electrodes were face identification sensitive. As in MPC, a multiple linear regression (r2 = 0.14) revealed that stronger face identity sensitivity was associated with greater face selectivity (β = 0.44; p < 10−5), whereas scene identity sensitivity was associated with greater place selectivity (β = −0.32; p < 0.001). Overall, the statistics for category and identification sensitivity were highly comparable between MTL and MPC. A MEMA contrasting responses during correct and incorrect naming trials for each stimulus type showed sites of identification sensitivity within MPC and MTL regions of interest (ROIs) (Figures 4 and S5). We also saw a prominent focus of activation for correct scene and face identity in mid-fusiform cortex, an area we and others have previously shown to be a crucial component of the lexico-semantic retrieval system [42Binder J.R. Desai R.H. Graves W.W. Conant L.L. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies.Cereb. Cortex. 2009; 19: 2767-2796Crossref PubMed Scopus (2226) Google Scholar, 43Conner C.R. Chen G. Pieters T.A. Tandon N. Category specific spatial dissociations of parallel processes underlying visual naming.Cereb. Cortex. 2014; 24: 2741-2750Crossref PubMed Scopus (25) Google Scholar, 44Forseth K.J. Kadipasaoglu C.M. Conner C.R. Hickok G. Knight R.T. Tandon N. A lexical semantic hub for heteromodal naming in middle fusiform gyrus.Brain. 2018; 141: 2112-2126Crossref PubMed Scopus (34) Google Scholar]. Activation of the supplementary motor area and anterior cingulate cortex was greater for correct trials, corresponding to speech motor planning and name production. Of the 142 responsive electrodes in MPC, 53 (37%) showed only face identification sensitivity compared to 45 (32%) with only scene identification sensitivity. 18 (13%) showed significant identification responses to both categories (Figure 5A). Of the 216 responsive electrodes in MTL, 80 (37%) showed face identification sensitivity and 37 (17%) showed scene identification sensitivity. 14 (6%) showed a significant identification response for both categories. The MTL was less engaged in representation of scenes relative to face identification as compared to MPC (χ2 [1; N = 215] = 4.62; p = 0.032). The right MTL was more specialized for face rather than scene identification than the left MTL (RH 3.64 versus LH 1.65; χ2 [1; N = 126] = 3.96; p = 0.046), implying a right MTL lateralization for face recognition. In distinction, MPC showed no lateralization in scene versus face selectivity (RH 1.56 versus LH 1.07; χ2 [1; N = 102] = 0.845; p = 0.36). The time courses of BGA responses of electrodes that were face or scene identification sensitive were remarkably conserved across stimulus categories within each ROI (Figures 5B and 5C), even though these electrodes were spatially separated. Both regions also showed a beta band (11–20 Hz) suppression that reached its trough just after the peak BGA response (Figure 5D). A remaining question is how MPC and MTL interact to enable identification and whether this process occurs earlier in one of the regions. To test this, we evaluated a subset of patients (n = 16) with at least one face-identity-sensitive electrode in both MPC and MTL. We evaluated two time points within each region: the first time point at which gamma traces were significantly different between faces and scrambled faces and the first point at which they were different for correctly versus incorrectly identified faces. In MPC, median latency for face discrimination was 329 ms (306–386 ms; 95% CI) and the latency for identification was 430 ms (403–523 ms). In the MTL, the latency of face discrimination was 340 ms (311–395 ms) and the latency of identification was 425 ms (325–488 ms). We generated bootstrapped distributions (Figure 5F) from this analysis to show that peak latencies for each contrast occur near simultaneously in both MPC and MTL. Figure 5G shows the distribution of latency differences in trial-paired MPC and MTL responses. The median difference was −20 ms (−98–46 ms) for face determination and 10.5 ms (−80–137 ms) for identity. Neither difference was significant (p = 0.77; p = 0.93, respectively). In the subset of patients with at least one scene-identity-selective electrode in each region (n = 9 patients), the analysis did not show any time points with a significant difference between correct and incorrect trials. To directly probe the interactions between these regions, we measured changes in the phase-locking value (PLV) of theta oscillations (3–5 Hz) [45Foster B.L. Kaveh A. Dastjerdi M. Miller K.J. Parvizi J. Human retrosplenial cortex displays transient theta phase locking with medial temporal cortex prior to activation during autobiographical memory retrieval.J. Neurosci. 2013; 33: 10439-10446Crossref PubMed Scopus (64) Google Scholar, 46Fuentemilla L. Barnes G.R. Düzel E. Levine B. Theta oscillations orchestrate medial temporal lobe and neocortex in remembering autobiographical memories.Neuroimage. 2014; 85: 730-737Crossref PubMed Scopus (59) Google Scholar, 47Solomon E.A. Kragel J.E. Sperling M.R. Sharan A. Worrell G. Kucewicz M. Inman C.S. Lega B. Davis K.A. Stein J.M. et al.Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition.Nat. Commun. 2017; 8: 1704Crossref PubMed Scopus (60) Google Scholar, 48Watrous A.J. Tandon N. Conner C.R. Pieters T. Ekstrom A.D. Frequency-specific network connectivity increases underlie accurate spatiotemporal memory retrieval.Nat. Neurosci. 2013; 16: 349-356Crossref PubMed Scopus (186) Google Scholar] between MPC and MTL during these tasks" @default.
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• W3033588700 date "2020-07-01" @default.
• W3033588700 modified "2023-10-14" @default.
• W3033588700 title "Category Selectivity for Face and Scene Recognition in Human Medial Parietal Cortex" @default.
• W3033588700 cites W1527850116 @default.
• W3033588700 cites W1535802865 @default.
• W3033588700 cites W1567857970 @default.
• W3033588700 cites W1619178479 @default.
• W3033588700 cites W1671674587 @default.
• W3033588700 cites W1894869667 @default.
• W3033588700 cites W1941589899 @default.
• W3033588700 cites W1963773703 @default.
• W3033588700 cites W1965082316 @default.
• W3033588700 cites W1965683618 @default.
• W3033588700 cites W1966964013 @default.
• W3033588700 cites W1972380851 @default.
• W3033588700 cites W1978339901 @default.
• W3033588700 cites W1978503387 @default.
• W3033588700 cites W1982567191 @default.
• W3033588700 cites W1984485854 @default.
• W3033588700 cites W1991490575 @default.
• W3033588700 cites W1991771535 @default.
• W3033588700 cites W1994089589 @default.
• W3033588700 cites W1999517339 @default.
• W3033588700 cites W2007239727 @default.
• W3033588700 cites W2011430633 @default.
• W3033588700 cites W2013179750 @default.
• W3033588700 cites W2015393008 @default.
• W3033588700 cites W2017860220 @default.
• W3033588700 cites W2022099781 @default.
• W3033588700 cites W2025484736 @default.
• W3033588700 cites W2028100214 @default.
• W3033588700 cites W2031338443 @default.
• W3033588700 cites W2031694597 @default.
• W3033588700 cites W2032084555 @default.
• W3033588700 cites W2032847834 @default.
• W3033588700 cites W2039225803 @default.
• W3033588700 cites W2040891545 @default.
• W3033588700 cites W2041078406 @default.
• W3033588700 cites W2042280137 @default.
• W3033588700 cites W2043251067 @default.
• W3033588700 cites W2050099296 @default.
• W3033588700 cites W2059179319 @default.
• W3033588700 cites W2062081744 @default.
• W3033588700 cites W2077970457 @default.
• W3033588700 cites W2106608293 @default.
• W3033588700 cites W2107417492 @default.
• W3033588700 cites W2107722806 @default.
• W3033588700 cites W2109558135 @default.
• W3033588700 cites W2111613011 @default.
• W3033588700 cites W2112891119 @default.
• W3033588700 cites W2113319997 @default.
• W3033588700 cites W2117140276 @default.
• W3033588700 cites W2119630138 @default.
• W3033588700 cites W2123341385 @default.
• W3033588700 cites W2133663463 @default.
• W3033588700 cites W2144631174 @default.
• W3033588700 cites W2144741543 @default.
• W3033588700 cites W2149577107 @default.
• W3033588700 cites W2150872839 @default.
• W3033588700 cites W2151721316 @default.
• W3033588700 cites W2152160545 @default.
• W3033588700 cites W2152691873 @default.
• W3033588700 cites W2158114530 @default.
• W3033588700 cites W2329251224 @default.
• W3033588700 cites W2413793810 @default.
• W3033588700 cites W2441659230 @default.
• W3033588700 cites W2499800833 @default.
• W3033588700 cites W2510137017 @default.
• W3033588700 cites W2593866152 @default.
• W3033588700 cites W2621284211 @default.
• W3033588700 cites W2737241688 @default.
• W3033588700 cites W2770613913 @default.
• W3033588700 cites W2794376005 @default.
• W3033588700 cites W2805145149 @default.
• W3033588700 cites W2895998312 @default.
• W3033588700 cites W2918972315 @default.
• W3033588700 cites W2942764482 @default.
• W3033588700 cites W2951109889 @default.
• W3033588700 cites W2951137108 @default.
• W3033588700 cites W2958467663 @default.
• W3033588700 cites W2958845658 @default.
• W3033588700 cites W2964813937 @default.
• W3033588700 cites W2971683016 @default.
• W3033588700 cites W3002338557 @default.
• W3033588700 cites W3006162085 @default.
• W3033588700 cites W4233322041 @default.
• W3033588700 cites W4246364355 @default.
• W3033588700 doi "https://doi.org/10.1016/j.cub.2020.05.018" @default.
• W3033588700 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32502406" @default.
• W3033588700 hasPublicationYear "2020" @default.

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