Daniel Fraiman

Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis

Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of gradual and continuous changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in discrete events. This idea is further explored here to demonstrate how brain dynamics at resting state can be captured just by the timing and location of such events, i.e., in terms of a spatiotemporal point process. The method allows, for the first time, to define a theoretical framework in terms of an order and control parameter derived from fMRI data, where the dynamical regime can be interpreted as one corresponding to a system close to the critical point of a second order phase transition. The analysis demonstrates that the resting brain spends most of the time near the critical point of such transition and exhibits avalanches of activity ruled by the same dynamical and statistical properties described previously for neuronal events at smaller scales. Given the demonstrated functional relevance of the resting state brain dynamics, its representation as a discrete process might facilitate large-scale analysis of brain function both in health and disease.

Brain resting state is disrupted in chronic back pain patients.

Recent brain functional magnetic resonance imaging (fMRI) studies have shown that chronic back pain (CBP) alters brain dynamics beyond the feeling of pain. In particular, the response of the brain default mode network (DMN) during an attention task was found abnormal. In the present work similar alterations are demonstrated for spontaneous resting patterns of fMRI brain activity over a population of CBP patients (n=12, 29-67 years old, mean=51.2). Results show abnormal correlations of three out of four highly connected sites of the DMN with bilateral insular cortex and regions in the middle frontal gyrus (p<0.05), in comparison with a control group of healthy subjects (n=20, 21-60 years old, mean=38.4). The alterations were confirmed by the calculation of triggered averages, which demonstrated increased coactivation of the DMN and the former regions. These findings demonstrate that CBP disrupts normal activity in the DMN even during the brain resting state, highlighting the impact of enduring pain over brain structure and function.

Disrupted Functional Connectivity of the Pain Network in Fibromyalgia

Objective To investigate the impact of chronic pain on brain dynamics at rest. Methods Functional connectivity was examined in patients with fibromyalgia (FM) (n = 9) and healthy controls (n = 11) by calculating partial correlations between low-frequency blood oxygen level–dependent fluctuations extracted from 15 brain regions. Results Patients with FM had more positive and negative correlations within the pain network than healthy controls. Patients with FM displayed enhanced functional connectivity of the anterior cingulate cortex (ACC) with the insula (INS) and basal ganglia (p values between .01 and .05), the secondary somatosensory area with the caudate (CAU) (p = .012), the primary motor cortex with the supplementary motor area (p = .007), the globus pallidus with the amygdala and superior temporal sulcus (both p values < .05), and the medial prefrontal cortex with the posterior cingulate cortex (PCC) and CAU (both p values < .05). Functional connectivity of the ACC with the amygdala and periaqueductal gray (PAG) matter (p values between .001 and .05), the thalamus with the INS and PAG (both p values < .01), the INS with the putamen (p = .038), the PAG with the CAU (p = .038), the secondary somatosensory area with the motor cortex and PCC (both p values < .05), and the PCC with the superior temporal sulcus (p = .002) was also reduced in FM. In addition, significant negative correlations were observed between depression and PAG connectivity strength with the thalamus (r = −0.64, p = .003) and ACC (r = −0.60, p = .004). Conclusions These findings demonstrate that patients with FM display a substantial imbalance of the connectivity within the pain network during rest, suggesting that chronic pain may also lead to changes in brain activity during internally generated thought processes such as occur at rest. Abbreviations BOLD = blood oxygen level–dependent; FM = fibromyalgia; HC = healthy control; WHYMPI = West Haven-Yale Multidimensional Pain Inventory; fMRI = functional magnetic resonance imaging; ACC = anterior cingulate cortex; PCC = posterior cingulate cortex; AMYG = amygdala; CAU = caudate; PUT = putamen; INS = insula; M1 = primary motor area; SMA = supplementary motor area; SI = primary somatosensory area; SII = secondary somatosensory area; mPFC = medial prefrontal cortex; PAG = periaqueductal gray; STS = superior temporal sulcus; THA = thalamus

What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations

The study of spontaneous fluctuations of brain activity, often referred as brain noise, is getting increasing attention in functional magnetic resonance imaging (fMRI) studies. Despite important efforts, much of the statistical properties of such fluctuations remain largely unknown. This work scrutinizes these fluctuations looking at specific statistical properties which are relevant to clarify its dynamical origins. Here, three statistical features which clearly differentiate brain data from naive expectations for random processes are uncovered: First, the variance of the fMRI mean signal as a function of the number of averaged voxels remains constant across a wide range of observed clusters sizes. Second, the anomalous behavior of the variance is originated by bursts of synchronized activity across regions, regardless of their widely different sizes. Finally, the correlation length (i.e., the length at which the correlation strength between two regions vanishes) as well as mutual information diverges with the clusters size considered, such that arbitrarily large clusters exhibit the same collective dynamics than smaller ones. These three properties are known to be exclusive of complex systems exhibiting critical dynamics, where the spatio-temporal dynamics show these peculiar type of fluctuations. Thus, these findings are fully consistent with previous reports of brain critical dynamics, and are relevant for the interpretation of the role of fluctuations and variability in brain function in health and disease.

Modular Organization of Brain Resting State Networks in Chronic Back Pain Patients

Recent work on functional magnetic resonance imaging large-scale brain networks under resting conditions demonstrated its potential to evaluate the integrity of brain function under normal and pathological conditions. A similar approach is used in this work to study a group of chronic back pain patients and healthy controls to determine the impact of long enduring pain over brain dynamics. Correlation networks were constructed from the mutual partial correlations of brain activitys time series selected from ninety regions using a well validated brain parcellation atlas. The study of the resulting networks revealed an organization of up to six communities with similar modularity in both groups, but with important differences in the membership of key communities of frontal and temporal regions. The bulk of these findings were confirmed by a surprisingly naive analysis based on the pairwise correlations of the strongest and weakest correlated healthy regions. Beside confirming the brain effects of long enduring pain, these results provide a framework to study the effect of other chronic conditions over cortical function.

The Brain: What is Critical About It?

We review the recent proposal that the most fascinating brain properties are related to the fact that it always stays close to a second order phase transition. In such conditions, the collective of neuronal groups can reliably generate robust and flexible behavior, because it is known that at the critical point there is the largest abundance of metastable states to choose from. Here we review the motivation, arguments and recent results, as well as further implications of this view of the functioning brain.

Stroke and Neurodegeneration Induce Different Connectivity Aberrations in the Insula

Background and Purpose— Stroke and neurodegeneration cause significant brain damage and cognitive impairment, especially if the insular cortex is compromised. This study explores for the first time whether these 2 causes differentially alter connectivity patterns in the insular cortex. Methods— Resting state–functional magnetic resonance imaging data were collected from patients with insular stroke, patients with behavioral variant frontotemporal dementia, and healthy controls. Data from the 3 groups were assessed through a correlation function analysis. Specifically, we compared decreases in connectivity as a function of voxel Euclidean distance within the insular cortex. Results— Relative to controls, patients with stroke showed faster connectivity decays as a function of distance (hypoconnectivity). In contrast, the behavioral variant frontotemporal dementia group exhibited significant hyperconnectivity between neighboring voxels. Both patient groups evinced global hypoconnectivity. No between-group differences were observed in a volumetrically and functionally comparable region without ischemia or neurodegeneration. Conclusions— Functional insular cortex connectivity is affected differently by cerebral ischemia and neurodegeneration, possibly because of differences in the cause-specific pathophysiological mechanisms of each disease. These findings have important clinical and theoretical implications.

Biological Motion Coding in the Brain: Analysis of Visually Driven EEG Functional Networks

Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM). Whereas global network measures (average path length, average clustering coefficient, and average betweenness) computed as a function of time did not discriminate between BM and SM, local node properties did. Comparing the network local measures of the BM condition with those of the SM condition, we found higher degree and betweenness values in the left frontal (F7) electrode, as well as a higher clustering coefficient in the right occipital (O2) electrode, for the SM condition. Conversely, for the BM condition, we found higher degree values in central parietal (Pz) electrode and a higher clustering coefficient in the left parietal (P3) electrode. These results are discussed in the context of the brain networks involved in encoding BM versus SM.

Variability in functional brain networks predicts expertise during action observation

Abstract Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph‐theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task‐relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise‐dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2 s). In brief, variability in the organization of task‐related networks seems to be a highly sensitive indicator of long‐lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain‐specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research. HighlightsWe analyzed functional brain networks implicated in motor expertise.Networks showed less variability in highly skilled individuals.Network variability serves as a classifier to identify the level of motor expertise.Network variability is a fruitful measure in neuroimaging research.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.