Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions

Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions.Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest.Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10−7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal.Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.

The Role of the Human Brain Neuron–Glia–Synapse Composition in Forming Resting-State Functional Connectivity Networks

While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain’s cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood–oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units’ connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.

Aerobic glycolysis, the efficiency tradeoff hypothesis, and the biological basis of neuroimaging: A solution to a metabolic mystery at the heart of neuroscience.

Aerobic glycolysis is a form of glucose-inefficient metabolism that occurs when cells metabolize glucose without oxygen, despite oxygen being abundant; the result is less energy per glucose molecule and increased glucose consumption. Aerobic glycolysis in the brain is a metabolic paradox: this inefficient metabolic process is a hallmark of neural activity, yet brains supposedly evolved to be energy-efficient. We discuss this paradox and introduce a possible solution, formalized as the efficiency tradeoff hypothesis: aerobic glycolysis, despite using glucose inefficiently, allows for energy-efficient communication. It allows axon diameter to be minimized (decreasing energy costs of communication) while allowing energy production to closely adhere to unpredictable, rapid-on/rapid-off energy demands. We expand on this hypothesis—linking observations across levels of analysis, from cognitive function to its biological implementation in the brain—culminating in a novel interpretation of the blood-oxygen level-dependent (BOLD) signal, which is closely related to localized metabolic changes caused by aerobic glycolysis. We hypothesize that the BOLD signal indexes bottom-up sensory encoding, or more specifically, prediction error in predictive processing models. This implies that much of a brain’s function, which is implemented with predictive signaling, is not indexed by BOLD fMRI. We then elaborate on the implications of our account for (a) how the evolution of human cytoarchitecture may relate to metabolism and brain function, (b) how social behavior may depend on metabolic cost functions, and (c) how metabolism may play a fundamental role in mental illness. We conclude that aerobic glycolysis and the efficiency tradeoff hypothesis offer a generative foundation for future neuroscientific research.

BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery

Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22–56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients’ recovery represents a major step forward in prognostic development.

Correcting for Superficial Bias in 7T Gradient Echo fMRI

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.

Prolonged tonic pain in healthy humans enhances functional connectivity of descending pain modulation networks involving the amygdala, periaqueductal gray and parabrachial nucleus to cortical sensory-discriminative areas

IntroductionResting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC changes accompanying tonic pain in pain-free persons is rare. A brain network disrupted during chronic pain is a network we term the Descending Pain Modulatory Network (DPMN). Here, we evaluate the effect of tonic pain on FC of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN).MethodsIn 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. We used functional MRI to measure resting BOLD signal during pain-free rest where participants experienced warmth and tonic pain where participants experienced the same temperature thermode combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity with FC during both states. We hypothesized tonic pain would disrupt FC dyads within the DPMN. We used partial correlation to determine FC correlated with pain intensity and BOLD signal.ResultsOf hypothesized FC dyads, PAG and subgenual ACC was weakly disrupted during tonic pain (F=3.34; p=0.074; pain-free>pain d=0.25). sgACC-PAG FC became positively related to pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During tonic pain, FC of this connection was abolished (paired t=-3.17; p=0.0026). During pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During tonic pain, FC between left AMYG and right inferior temporal and superior frontal gyri negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC and right claustrum; left and right primary visual cortex; right middle temporal gyrus and right temporo-occipitoparietal junction. Finally, subsequent pain positively correlated with PAG FC and left cerebellum, left dorsolateral prefrontal, right posterior cingulate cortex and paracentral lobule, inferior parietal lobule, medial precuneus and PBN.ConclusionWe demonstrate 1) tonic pain weakly disrupts of sgACC-PAG FC; 2) sgACC-PAG tonic pain FC positively correlates with pain; 3) right PBN-PAG FC predicts subsequent pain and is abolished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which predict intensity of tonic pain. Our findings suggest specific connectivity patterns within the DPMN at rest predict experienced pain and are modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation and biomarkers to guide interventions.HighlightsParabrachial-periaqueductal gray (PAG) functional connectivity (FC) predicts painSubgenual anterior cingulate cortex-PAG FC correlates with pain during tonic painPAG- and amydalocortical networks at rest predict tonic pain intensityResting FC of PAG supports cortical targets of neuromodulation to control pain

Voxel-Wise Linearity Analysis of Increments and Decrements in BOLD Responses in Human Visual Cortex Using a Contrast Adaptation Paradigm

The linearity of BOLD responses is a fundamental presumption in most analysis procedures for BOLD fMRI studies. Previous studies have examined the linearity of BOLD signal increments, but less is known about the linearity of BOLD signal decrements. The present study assessed the linearity of both BOLD signal increments and decrements in the human primary visual cortex using a contrast adaptation paradigm. Results showed that both BOLD signal increments and decrements kept linearity to long stimuli (e.g., 3 s, 6 s), yet, deviated from linearity to transient stimuli (e.g., 1 s). Furthermore, a voxel-wise analysis showed that the deviation patterns were different for BOLD signal increments and decrements: while the BOLD signal increments demonstrated a consistent overestimation pattern, the patterns for BOLD signal decrements varied from overestimation to underestimation. Our results suggested that corrections to deviations from linearity of transient responses should consider the different effects of BOLD signal increments and decrements.