PaMiNI-Derived Co-Activation Patterns Indicate Differential Hierarchical Levels for Two Ventral Visual Areas of the Fusiform Gyrus

Abstract

Purpose: To investigate the distribution of co-activation patterns of the recently identified ventral visual areas FG1 and FG2 of the posterior fusiform gyrus using the novel meta-analytic approach PaMiNI (Pattern Mining in NeuroImaging).

Materials and Methods: All neuroimaging experiments reporting activation foci within FG1 or FG2 were retrieved from the BrainMap database. The stereotaxic activation foci in standard reference space were analyzed with PaMiNI. Here, Gaussian mixture modeling was applied to the stereotaxic coordinates of all foci to identify the underlying brain regions of each dataset. Then, association analysis was performed to reveal frequent co-activations across the modeled brain regions.

Results: Co-activation patterns of FG1 were mainly found within the visual system, i. e. in early visual areas, and were symmetrically distributed across both hemispheres. FG2 features several extra-visual co-activations, mainly to inferior frontal, premotor and parietal regions. Furthermore, the co-activations of FG2 showed clear lateralization to the left FG2.

Conclusion: FG1 shows characteristics of an intermediate visual area between the early ventral visual cortex and the category-specific higher-order areas. Co-activation patterns of FG2 indicate that FG2 is a higher-order visual area that probably corresponds to the posterior fusiform face area and partly the visual word-form area.

Key points

• Co-activation patterns of areas FG1 and FG2 were analyzed with PaMiNI.

• FG1 features mainly symmetric co-activations to areas of the visual system.

• FG2 shows several extra-visual co-activations, which are left-lateralized.

• FG1 corresponds to a hierarchically intermediate, FG2 to a higher-order visual area.

• The PaMiNI approach is extended to seed-specific mapping of co-activation patterns.

Citation Format:

• Caspers J, Eickhoff SB, Amunts K et al. PaMiNI-Derived Co-Activation Patterns Indicate Differential Hierarchical Levels for Two Ventral Visual Areas of the Fusiform Gyrus. Fortschr Röntgenstr 2015; 187: 892 – 898

Zusammenfassung

Ziel: Untersuchung der Verteilung von Koaktivierungsmustern der kürzlich entdeckten ventralen visuellen Areale FG1 und FG2 auf dem posterioren Gyrus fusiformis mit dem neuartigen meta-analytischen Verfahren PaMiNI (Pattern Mining in NeuroImaging).

Material und Methoden: Alle Neurobildgebungsstudien, die Aktivierungen in FG1 oder FG2 berichten, wurden aus der BrainMap Datenbank abgerufen. Die berichteten stereotaktischen Aktivierungsfoci im standardisierten Referenzraum wurden mit PaMiNI analysiert. Hierbei wurde Gaussian mixture modeling auf die dreidimensionalen Koordinaten der Aktivierungen angewandt, um die dem entsprechenden Datensatz zugrunde liegenden Hirnregionen zu identifizieren. Anschließend wurde eine Assoziationsanalyse durchgeführt, welche häufige Koaktivierungsmuster zwischen den modellierten Hirnregionen erkennen lässt.

Ergebnisse: Die Koaktivierungsmuster von FG1 waren hauptsächlich innerhalb des visuellen Systems zu finden, insbesondere in frühen visuellen Arealen, und sie waren symmetrisch bezüglich der beiden Hemisphären. FG2 wies zahlreiche extra-visuelle Koaktivierungen auf, vor allem zu interioren frontalen, prämotorischen und parietalen Regionen. Zudem zeigten die Koaktivierungen von FG2 eine klare Lateralisierung zur linken Seite.

Schlussfolgerung: In der Hierarchie des ventralen visuellen Systems zeigt FG1 Merkmale eines intermediären visuellen Areals zwischen den frühen visuellen und den Kategorie-spezifischen höheren Arealen. Die Koaktivierungen von FG2 weisen darauf hin, dass FG2 ein hierarchisch höheres Areal ist, welches wahrscheinlich der posterioren „fusiform face area“ und anteilig der „visual word-form area“ entspricht.

Kernaussagen

• Koaktivivierungsmuster von FG1 und FG2 wurden mit PaMiNI analysiert.

• FG1 weist weitgehend symmetrische Koaktivierungen mit Arealen des visuellen Systems auf.

• FG2 zeigt zahlreiche extra-visuelle Koaktivierungen, die links-lateralisiert sind.

• FG1 entspricht einem hierarchisch intermediären, FG2 einem höhergeordneten visuellen Areal.

• Der PaMiNI Ansatz wird um die Seed-spezifische Kartierung von Koaktivierungsmustern erweitert.

Key words

pattern mining in neuroimaging - brainmap database - coordinate-based meta-analysis - ventral visual cortex - fusiform face area - visual word-form area

• References

• 1 Grill-Spector K, Malach R. The human visual cortex. Annu Rev Neurosci 2004; 27: 649-677
• 2 Wandell BA, Winawer J. Imaging retinotopic maps in the human brain. Vision Res 2011; 51: 718-737
• 3 Grill-Spector K, Weiner KS. The functional architecture of the ventral temporal cortex and its role in categorization. Nat Rev Neurosci 2014; 15: 536-548
• 4 Caspers J, Zilles K, Eickhoff SB et al. Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus. Brain Struct Funct 2013; 218: 511-526
• 5 Caspers J, Palomero-Gallagher N, Caspers S et al. Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus. Brain Struct Funct 2015; 220: 205-219
• 6 Caspers J, Zilles K, Amunts K et al. Functional characterization and differential coactivation patterns of two cytoarchitectonic visual areas on the human posterior fusiform gyrus. Hum Brain Mapp 2014; 35: 2754-2767
• 7 Weiner KS, Golarai G, Caspers J et al. The mid-fusiform sulcus: a landmark identifying both cytoarchitectonic and functional divisions of human ventral temporal cortex. Neuroimage 2014; 84: 453-465
• 8 Cohen L, Dehaene S, Naccache L et al. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain 2000; 123: 291-307
• 9 Caspers J, Zilles K, Beierle C et al. A novel meta-analytic approach: mining frequent co-activation patterns in neuroimaging databases. Neuroimage 2014; 90: 390-402
• 10 Laird AR, Lancaster JL, Fox PT. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 2005; 3: 65-78
• 11 Eickhoff SB, Grefkes C. Approaches for the integrated analysis of structure, function and connectivity of the human brain. Clin EEG Neurosci 2011; 42: 107-121
• 12 Felleman DJ, van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1991; 1: 1-47
• 13 Caspers J, Zilles K, Eickhoff SB et al. Coordinate-Based Pattern-Mining on Functional Neuroimaging Databases. In: Greco S, Bouchon-Meunier B, Coletti G, et al. eds Advances on Computational Intelligence. Berlin Heidelberg: Springer; 2012: 240-249
• 14 Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological) 1977; 39: 1-38
• 15 Schwarz G. Estimating the dimension of a model. The annals of statistics 1978; 6: 461-464
• 16 Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc 20th Int Conf Very Large Data Bases, VLDB. 1994: 487-499
• 17 Caspers J, Zilles K, Eickhoff SB et al. PaMiNI: A comprehensive system for mining frequent neuronal patterns of the human brain. In: 25th International Symposium on Computer-Based Medical Systems (CBMS), 2012. 2012
• 18 Eickhoff SB, Paus T, Caspers S et al. Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. Neuroimage 2007; 36: 511-521
• 19 Rottschy C, Eickhoff SB, Schleicher A et al. Ventral visual cortex in humans: cytoarchitectonic mapping of two extrastriate areas. Hum Brain Mapp 2007; 28: 1045-1059
• 20 Malikovic A, Amunts K, Schleicher A et al. Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lp. Brain Struct Funct 2015;
• 21 Amunts K, Malikovic A, Mohlberg H et al. Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable?. Neuroimage 2000; 11: 66-84
• 22 Choi HJ, Zilles K, Mohlberg H et al. Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. J Comp Neurol 2006; 495: 53-69
• 23 Scheperjans F, Eickhoff SB, Homke L et al. Probabilistic maps, morphometry, and variability of cytoarchitectonic areas in the human superior parietal cortex. Cereb Cortex 2008; 18: 2141-2157
• 24 Amunts K, Schleicher A, Burgel U et al. Broca's region revisited: cytoarchitecture and intersubject variability. J Comp Neurol 1999; 412: 319-341
• 25 Geyer S, Schormann T, Mohlberg H et al. Areas 3a, 3b, and 1 of human primary somatosensory cortex. Part 2. Spatial normalization to standard anatomical space. Neuroimage 2000; 11: 684-696
• 26 Geyer S, Ledberg A, Schleicher A et al. Two different areas within the primary motor cortex of man. Nature 1996; 382: 805-807
• 27 Caspers S, Eickhoff SB, Geyer S et al. The human inferior parietal lobule in stereotaxic space. Brain Struct Funct 2008; 212: 481-495
• 28 Fox MD, Snyder AZ, Vincent JL et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005; 102: 9673-9678
• 29 Price CJ. The Functional Anatomy of Reading. In: Richard SJF, Karl JF, Christopher DF, et al. eds Human Brain Function (Second Edition). Burlington: Academic Press; 2004: 547-562
• 30 Robinson JL, Laird AR, Glahn DC et al. Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Hum Brain Mapp 2010; 31: 173-184