title: The spatiotemporal substrates of autobiographical recollection: Using event-related ICA to study cognitive networks in action.
abstract: Higher cognitive functions depend upon dynamically unfolding brain network interactions. Autobiographical recollection - the autonoetic re-experiencing of context rich, emotionally laden, personally experienced episodes - is an excellent example of such a process. Autobiographical recollection unfolds over time, with different cognitive processes engaged at different times throughout. In this paper we apply a recently developed analysis technique - event related independent components analysis (eICA) - to study the spatiotemporal dynamics of neural activity supporting autobiographical recollection. Participants completed an in-scanner autobiographical recollection paradigm in which the recalled episodes varied in chronological age and emotional content. By combining eICA with these cognitive manipulations we show that the brain-wide response to autobiographical recollection comprises brain networks with (i) different sensitivities to psychological aspects of the to-be-recollected material and (ii) distinct temporal profiles of activity during recollection. We identified networks with transient activations (in language and cognitive control related regions) and deactivations (in auditory and sensorimotor regions) to each autobiographical probe question, as well as networks with responses that are sustained over the course of the recollection period. These latter networks together overlapped spatially with the broader default mode network (DMN), indicating subspecialisation within the DMN. The vividness of participants' recollection was associated with the magnitude of activation in left dorsolateral prefrontal cortex and deactivation in visual association cortices. We interpret our results in the context of current theories of the spatial and temporal organisation of the human autobiographical memory system. Our findings demonstrate the utility of eICA as a tool for studying higher cognitive functions. The application of eICA to high spatial and temporal resolution datasets identifies in a single experimental protocol spatially specific networks that are recruited during cognitive activity, as well as the temporal order of activation of these networks.
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, VIC, Australia. Electronic address: chris.tailby@florey.edu.au.
authors.forename: Chris
authors.initials: C
authors.lastname: Tailby
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, VIC, Australia.
authors.forename: Genevieve
authors.initials: G
authors.lastname: Rayner
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, VIC, Australia.
authors.forename: Sarah
authors.initials: S
authors.lastname: Wilson
authors.affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia; Department of Neurology, Austin Health, Melbourne, VIC, Australia.
authors.forename: Graeme
authors.initials: G
authors.lastname: Jackson
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition / Q000502:physiology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D061212:Memory, Episodic; D011939:Mental Recall / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D012815:Signal Processing, Computer-Assisted
keywords: Autobiographical memory; Brain networks; Default mode network; Functional magnetic resonance imaging; Independent components analysis