Hierarchical modularity in human brain functional networks

The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions

Real-time motion analytics during brain MRI improve data quality and reduce costs

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more

Independent circuits in basal ganglia and cortex for the processing of reward and precision feedback

In order to understand human decision making it is necessary to understand how the brain uses feedback to guide goal-directed behavior. The ventral striatum (VS) appears to be a key structure in this function, responding strongly to explicit reward feedback. However, recent results have also shown striatal activity following correct task performance even in the absence of feedback. This raises the possibility that, in addition to processing external feedback, the dopamine-centered reward circuit might regulate endogenous reinforcement signals, like those triggered by satisfaction in accurate task performance. Here we use functional magnetic resonance imaging (fMRI) to test this idea. Participants completed a simple task that garnered both reward feedback and feedback about the precision of performance. Importantly, the design was such that we could manipulate information about the precision of performance within different levels of reward magnitude. Using parametric modulation and functional connectivity analysis we identified brain regions sensitive to each of these signals. Our results show a double dissociation: frontal and posterior cingulate regions responded to explicit reward but were insensitive to task precision, whereas the dorsal striatum - and putamen in particular - was insensitive to reward but responded strongly to precision feedback in reward-present trials. Both types of feedback activated the VS, and sensitivity in this structure to precision feedback was predicted by personality traits related to approach behavior and reward responsiveness. Our findings shed new light on the role of specific brain regions in integrating different sources of feedback to guide goal-directed behavior

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

Reorganization of retinotopic maps after occipital lobe infarction

Published in final edited form as: J Cogn Neurosci. 2014 June ; 26(6): 1266–1282. doi:10.1162/jocn_a_00538.We studied patient JS, who had a right occipital infarct that encroached on visual areas V1, V2v, and VP. When tested psychophysically, he was very impaired at detecting the direction of motion in random dot displays where a variable proportion of dots moving in one direction (signal) were embedded in masking motion noise (noise dots). The impairment on this motion coherence task was especially marked when the display was presented to the upper left (affected) visual quadrant, contralateral to his lesion. However, with extensive training, by 11 months his threshold fell to the level of healthy participants. Training on the motion coherence task generalized to another motion task, the motion discontinuity task, on which he had to detect the presence of an edge that was defined by the difference in the direction of the coherently moving dots (signal) within the display. He was much better at this task at 8 than 3 months, and this improvement was associated with an increase in the activation of the human MT complex (hMT^+) and in the kinetic occipital region as shown by repeated fMRI scans. We also used fMRI to perform retinotopic mapping at 3, 8, and 11 months after the infarct. We quantified the retinotopy and areal shifts by measuring the distances between the center of mass of functionally defined areas, computed in spherical surface-based coordinates. The functionally defined retinotopic areas V1, V2v, V2d, and VP were initially smaller in the lesioned right hemisphere, but they increased in size between 3 and 11 months. This change was not found in the normal, left hemisphere of the patient or in either hemispheres of the healthy control participants. We were interested in whether practice on the motion coherence task promoted the changes in the retinotopic maps. We compared the results for patient JS with those from another patient (PF) who had a comparable lesion but had not been given such practice. We found similar changes in the maps in the lesioned hemisphere of PF. However, PF was only scanned at 3 and 7 months, and the biggest shifts in patient JS were found between 8 and 11 months. Thus, it is important to carry out a prospective study with a trained and untrained group so as to determine whether the patterns of reorganization that we have observed can be further promoted by training.This work was supported by NIH grant R01NS064100 to L. M. V. Lucia M. Vaina dedicates this article to Charlie Gross, who has been a long-time collaborator and friend. I met him at the INS meeting in Beaune (France), and since then we often discussed the relationship between several aspects of high-level visual processing described in his work in monkeys physiology and my work in neuropsychology. In particular, his pioneering study of biological motion in monkeys' superior temporal lobe has influenced my own work on biological motion and has led us to coauthor a paper on this topic. Working with Charlie was a uniquely enjoyable experience. Alan Cowey and I often spoke fondly about Charlie, a dear friend and close colleague to us both, whose work, exquisite sense of humor, and unbound zest of living we both deeply admired and loved. (R01NS064100 - NIH)Accepted manuscrip