A Quest for Meaning in Spontaneous Brain Activity - From fMRI to Electrophysiology to Complexity Science

The brain is not a silent, complex input/output system waiting to be driven by external stimuli; instead, it is a closed, self-referential system operating on its own with sensory information modulating rather than determining its activity. Ongoing spontaneous brain activity costs the majority of the brain\u27s energy budget, maintains the brain\u27s functional architecture, and makes predictions about the environment and the future. I have completed three separate studies on the functional significance and the organization of spontaneous brain activity. The first study showed that strokes disrupt large-scale network coherence in the spontaneous functional magnetic resonance imaging: fMRI) signals, and that the degree of such disruption predicts the behavioral impairment of the patient. This study established the functional significance of coherent patterns in the spontaneous fMRI signals. In the second study, by combining fMRI and electrophysiology in neurosurgical patients, I identified the neurophysiological signal underlying the coherent patterns in the spontaneous fMRI signal, the slow cortical potential: SCP). The SCP is a novel neural correlate of the fMRI signal, most likely underlying both spontaneous fMRI signal fluctuations and task-evoked fMRI responses. Some theoretical considerations have led me to propose a hypothesis on the involvement of the neural activity indexed by the SCP in the emergence of consciousness. In the last study I investigated the temporal organization across a wide range of frequencies in the spontaneous electrical field potentials recorded from the human brain. This study demonstrated that the arrhythmic, scale-free brain activity often discarded in human and animal electrophysiology studies in fact contains rich, complex structures, and further provided evidence supporting the functional significance of such activity

Average is optimal: An inverted-U relationship between trial-to-trial brain activity and behavioral performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance

Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks

International audienceStudies employing functional connectivity-type analyses have established that sponta-neous fluctuations in functional magnetic resonance imaging (fMRI) signals are orga-nized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra – a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework – a multivariate extension of the univari-ate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest fre-quencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional Rev.#1, Q

Volition and Action in the Human Brain: Processes, Pathologies, and Reasons

Humans seem to decide for themselves what to do, and when to do it. This distinctive capacity may emerge from an ability, shared with other animals, to make decisions for action that are related to future goals, or at least free from the constraints of immediate environmental inputs. Studying such volitional acts proves a major challenge for neuroscience. This review highlights key mechanisms in the generation of voluntary, as opposed to stimulus-driven actions, and highlights three issues. The first part focuses on the apparent spontaneity of voluntary action. The second part focuses on one of the most distinctive, but elusive, features of volition, namely, its link to conscious experience, and reviews stimulation and patient studies of the cortical basis of conscious volition down to the single-neuron level. Finally, we consider the goal-directedness of voluntary action, and discuss how internal generation of action can be linked to goals and reasons

A Behavioral Analysis of Spatial Neglect and its Recovery After Stroke

In a longitudinal study of recovery of left neglect following stroke using reaction time computerized assessment, we find that lateralized spatial deficits of attention and perception to be more severe than disturbance of action. Perceptual-attention deficits also show the most variability in the course of recovery, making them prime candidates for intervention. In an anatomical analysis of MRI findings, ventral frontal cortex damage was correlated with the most severe neglect, reflecting impaired fronto-parietal communication

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus.

MicroRNAs (miRNAs) are small noncoding RNAs that modulate the cellular transcriptome at the post-transcriptional level. miRNA plays important roles in different disease manifestation, including type 2 diabetes mellitus (T2DM). Many studies have characterized the changes of miRNAs in T2DM, a complex systematic disease; however, few studies have integrated these findings and explored the functional effects of the dysregulated miRNAs identified. To investigate the involvement of miRNAs in T2DM, we obtained and analyzed all relevant studies published prior to 18 October 2016 from various literature databases. From 59 independent studies that met the inclusion criteria, we identified 158 dysregulated miRNAs in seven different major sample types. To understand the functional impact of these deregulated miRNAs, we performed targets prediction and pathway enrichment analysis. Results from our analysis suggested that the altered miRNAs are involved in the core processes associated with T2DM, such as carbohydrate and lipid metabolisms, insulin signaling pathway and the adipocytokine signaling pathway. This systematic survey of dysregulated miRNAs provides molecular insights on the effect of deregulated miRNAs in different tissues during the development of diabetes. Some of these miRNAs and their mRNA targets may have diagnostic and/or therapeutic utilities in T2DM

A gradient of sharpening effects by perceptual prior across the human cortical hierarchy

Prior knowledge profoundly influences perceptual processing. Previous studies have revealed consistent suppression of predicted stimulus information in sensory areas, but how prior knowledge modulates processing higher up in the cortical hierarchy remains poorly understood. In addition, the mechanism leading to suppression of predicted sensory information remains unclear, and studies thus far have revealed a mixed pattern of results in support of either the "sharpening" or "dampening" model. Here, using 7T fMRI in humans (both sexes), we observed that prior knowledge acquired from fast, one-shot perceptual learning sharpens neural representation throughout the ventral visual stream, generating suppressed sensory responses. In contrast, the frontoparietal and default mode networks exhibit similar sharpening of content-specific neural representation, but in the context of unchanged and enhanced activity magnitudes, respectively: a pattern we refer to as "selective enhancement." Together, these results reveal a heretofore unknown macroscopic gradient of prior knowledge's sharpening effect on neural representations across the cortical hierarchy

Reduction of trial-to-trial variability following stimulus onset.

<p>(<b>A</b>) Averaged ERPs (top) and trial-to-trial variability time courses (bottom) for all 24 Laplacian electrodes from Pt #4 (contralateral data). The variability time course was computed as standard deviation (s.d.) across trials, normalized to the mean of the pre-stimulus period (−500∼0 ms) and expressed in %change unit. Thick black traces denote the average across 24 electrodes. (<b>B</b>) Top: Trial-to-trial variability time course averaged across all 153 Laplacian electrodes in five subjects. Dashed lines depict mean±SEM. Bottom: Significance of the variability time course, assessed by a one-sample t-test across 153 electrodes against the null hypothesis of no change from baseline. The left column is obtained using contralateral data, and the right column using ipsilateral data. Red dashed lines indicate significance level of P = 0.001.</p

Analysis combining all electrodes (contralateral data).

<p>(<b>A</b>) Top: For each subject, <i>D(t)</i> was computed by combining across all significant electrodes in the electrode-based analysis (orange and white electrodes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g005" target="_blank">Fig. 5A</a>), and then averaged for hit and miss trials separately. Flanking dashed lines depict mean±SEM. Red dots: P<0.005 for hit vs. miss trials, two-sample t-test. Bottom: the P-value time course of the two-sample t-test comparing <i>D(t)</i> between hit and miss trials. Dashed red line indicates significance level of P = 0.005. (<b>B</b>) As in (A), except that <i>D(t)</i> was computed by combining across all remaining non-significant electrodes not included in (A). (<b>C</b>) As in (A), except that <i>D(t)</i> was computed by combining across all electrodes. For (A–C), all subjects except Pt #3 were included, because Pt #3 did not have any electrode showing a significant quadratic ECoG-hit rate relationship (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g005" target="_blank">Fig. 5A</a>). (<b>D</b>) Pearson correlation coefficient between <i>D(t)</i> and RT across all hit trials (pooled across all five subjects). <i>D(t)</i> was combined across all significant electrodes from the electrode-based analysis (orange/white/yellow electrodes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003348#pcbi-1003348-g006" target="_blank">Fig. 6B</a>) (red line), all remaining non-significant electrodes (blue line) and all (black line) electrodes. Dots at the bottom: P<0.005 for significant <i>D(t)</i>-RT correlation, with <i>D(t)</i> computed using all significant (red), all non-significant (blue) or all (black) electrodes. Vertical dashed line indicates the time of median RT across all subjects.</p