Academic journal article Psychology in Russia

Large-Scale Network Analysis of Imagination Reveals Extended but Limited Top-Down Components in Human Visual Cognition

Academic journal article Psychology in Russia

Large-Scale Network Analysis of Imagination Reveals Extended but Limited Top-Down Components in Human Visual Cognition

Article excerpt

Introduction

"In the night, imagining some fear, how easy is a bush supposed a bear"

W. Shakespeare: A Midsummer Nights Dream (Act 5, Scene 1)

Current fMRI studies usually aim to identify brain structures in which the bloodoxygen-level dependent (BOLD) response covaries with the subjects performance in a specific cognitive task. However, this outcome cannot often be satisfactorily achieved because of the sparsity of network interrelations on a small scale (Smith, Vidaurre, Beckmann, Glasser, Jenkinson, Miller, Nichols, Robinson, Salimi-Khorshidi, Woolrich, Barch, Ugurbil, & Van Essen, 2013). A better solution can then be found by studying the whole brain and large-scale network activity. This macroscopic approach has already resulted in some important discoveries, including the identification of resting state networks (RSNs) (Barkhof, Haller, & Rombouts, 2014; Jann, Kottlow, Dierks, Boesch, & Koenig, 2010). RSN activity maybe the basis of higher-order thoughts and personal worries, processes that were long considered too elusive to be investigated under laboratory conditions. Another important development was the discovery of the mirror neuron system (MNS), an extended cortical network that has been implicated in the mediation of imitation learning, language acquisition, internal dialogue and even self-consciousness (Rizzolatti, 2005).

In this study, we apply this macroscopic strategy to the long-standing problem of the interaction between perception and imagination. This issue has been the subject of intensive discussions since the early days of experimental psychology. For example, such classics as Titchener and Kuelpe held opposite opinions on the role of visual imagery in the detection of near threshold visual objects (see Velichkovsky, 2006). Even 100 years later, the application of electrophysiological methods did not solve the problem due to their low spatial resolution. However, it has been repeatedly shown on the basis of psychophysical, electrophysiological and neuroimaging data that some early stages of visual processing could be influenced by top-down cognitive processes (Van der Stigchel, Belopolsky, Peters, Wijnen, Meeter, & Theeuwes, 2009; Cichy, Heinzle, & Haynes, 2012). We aimed to investigate the exact nature of these "early stages" and their underlying neural mechanisms.

Method

Participants and experimental design

Twenty-one healthy volunteers (13 males; mean age 23 years; age range: 20-38 years, students and teachers of Moscow universities) participated in the study. All participants were right-handed and had no history of neurological or psychiatric disorders. The study was approved by the local Ethical Committee of the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences. For the material we used familiar ("lecture") and non-familiar ("parachute jump") video clips. A block design consisted of 6 blocks: 3 baseline blocks alternating with3 experimental blocks. The duration of each block was 30 sec allowing for 10 fMRI scans (one scan every 3 sec). Nine different tasks as combinations of baseline and experimental blocks were investigated in the same temporal order for all subjects: 1) fixation cross + jump imagination, 2) fixation cross + lecture imagination, 3) fixation cross + jump viewing, 4) fixation cross + lecture viewing, 5) lecture viewing + jump viewing, 6) jump viewing + jump imagination, 7) lecture viewing + lecture imagination, 8) fixation cross + jump imagination/remembering, and 9) fixation cross + lecture imagination/remembering. The duration of the whole series of tasks usually did not exceed 45 minutes.

Data acquisition and analysis

Time series of 60 repeated whole brain fMRI images were acquired using T2*-weighted GRE-EPI sequence (TR= 3000 ms, TE = 35 ms, FOV=24x24 cm2,30 slices with thickness of 4 mm, gap= 1 mm, pixel size= 1.8 x 1.8 mm2) in 3.0T Achieva Philips MRI scanner. Anatomical data (T1 MP RAGE sequence with TR=8. …

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