Development of the triadic neural systems involved in risky decision-making during childhood.

Risk-taking often occurs in childhood as a compex outcome influenced by individual, family, and social factors. The ability to govern risky decision-making in a balanced manner is a hallmark of the integrity of cognitive and affective development from childhood to adulthood. The Triadic Neural Systems Model posits that the nuanced coordination of motivational approach, avoidance and prefrontal control systems is crucial to regulate adaptive risk-taking and related behaviors. Although widely studied in adolescence and adulthood, how these systems develop in childhood remains elusive. Here, we show heterogenous age-related differences in the triadic neural systems involved in risky decision-making in 218 school-age children relative to 80 young adults. Children were generally less reward-seeking and less risk-taking than adults, and exhibited gradual increases in risk-taking behaviors from 6 to 12 years-old, which are associated with age-related differences in brain activation patterns underlying reward and risk processing. In comparison to adults, children exhibited weaker activation in control-related prefrontal systems, but stronger activation in reward-related striatal systems. Network analyses revealed that children showed greater reward-related functional connectivity within and between the triadic systems. Our findings support an immature and unbalanced developmental view of the core neurocognitive systems involved in risky decision-making and related behaviors in middle to late childhood.

Assessment of MRI-based anomalous diffusion changes in brain ischemic stroke with a fractional motion model.

The actual diffusion process in human brain has been shown to be anomalous comparing to that predicted with traditional diffusion MRI (dMRI) theory. Recently, dMRI based on fractional motion (FM) model has demonstrated the potential to accurately describe anomalous diffusion in vivo. In this work, we explored the potential value of FM model-based dMRI in quantificational identification of ischemic stroke and compared that with the traditional apparent diffusion coefficient (ADC). We included 23 acute stroke patients, 8 of whom finished a follow-up scan, and 22 matched healthy controls. The dMRI images were acquired by using a Stejskal-Tanner single-shot spin-echo echo-planar-imaging sequence (diffusion gradients were applied in three orthogonal directions with 25 non-zero b values ranging from 248 to 4474 s/mm2) at 3.0 T MRI. We calculated the coefficient of variation (CV) for FM-related parameters in stroke lesions, and compared the mean values for FM-related parameters and ADC by using two-sample t-tests. Correlation analysis was achieved using Pearson correlation coefficient test. In acute stroke lesions, CV for FM-related parameters showed significant increase compared with normal tissues (P < 0.01), while those of ADC didn't appear statistical difference. Mean values for FM-related parameters showed significant decrease in acute lesion (P < 0.01) and their changing pattern during follow-up was positively correlated with ADC (P < 0.005). Our results initially verified the utility of the FM-model in detecting ischemic stroke compared with traditional dMRI.

Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks.

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5-min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network-switching pattern (i.e., transition probability and mean interval time) and the time-allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5-48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.

EEG/MEG source imaging using fMRI informed time-variant constraints.

Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) or magnetoencephalography (MEG) is able to provide high spatiotemporal resolution mapping of brain activity. However, the accuracy of fMRI-constrained EEG/MEG source imaging may be degraded by potential spatial mismatches between the locations of fMRI activation and electrical source activities. To address this problem, we propose a novel fMRI informed time-variant constraint (FITC) method. The weights in FITC are determined by combining the fMRI activities and electrical source activities in a time-variant manner to reduce the impact of the fMRI extra sources. The fMRI weights are modified using cross-talk matrix and normalized partial area under the curve to reduce the impact of fMRI missing sources. Monte Carlo simulations were performed to compare the source estimates produced by L2-minimum norm estimation (MNE), fMRI-weighted minimum norm estimation (fMNE), FITC, and depth-weighted FITC (wFITC) algorithms with various spatial mismatch conditions. Localization error and temporal correlation were calculated to compare the four algorithms under different conditions. The simulation results indicated that the FITC and wFITC methods were more robust than the MNE and fMNE algorithms. Moreover, FITC and wFITC were significantly better than fMNE under the fMRI missing sources condition. A human visual-stimulus EEG, MEG, and fMRI test was performed, and the experimental data revealed that FITC and wFITC displayed more focal areas than fMNE and MNE. In conclusion, the proposed FITC method is able to better resolve the spatial mismatch problems encountered in fMRI-constrained EEG/MEG source imaging.

Disparity of activation onset in sensory cortex from simultaneous auditory and visual stimulation: Differences between perfusion and blood oxygenation level-dependent functional magnetic resonance imaging.

PURPOSE: To compare the temporal behaviors of perfusion and blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in the detection of timing differences between distinct brain areas, and determine potential latency differences between stimulus onset and measurable fMRI signal in sensory cortices. MATERIALS AND METHODS: Inversion recovery (IR) spin-echo echo-planar imaging (EPI) and T2*-weighted gradient-echo EPI sequences were used for perfusion- and BOLD-weighted experiments, respectively. Simultaneous auditory and visual stimulations were employed in an event-related (ER) paradigm. Signal time courses were averaged across 40 repeated trials to evaluate the onset of activation and to determine potential differences of activation latency between auditory and visual cortices and between these scanning methods. RESULTS: Temporal differences between visual and auditory areas ranged from 90-200 msec (root-mean-square (RMS) = 134 msec) and from -80 to 930 msec (RMS = 604 msec) in perfusion and BOLD measurements, respectively. The temporal variability detected with BOLD sequences was larger between subjects and was significantly greater than that in the perfusion response (P < 0.04). The measured time to half maximum (TTHM) values for perfusion imaging (visual, 3260 +/- 710 msec; auditory, 3130 +/- 700 msec) were earlier than those in BOLD responses (visual, 3770 +/- 430 msec; auditory, 3360 +/- 460 msec). CONCLUSION: The greater temporal variability between brain areas detected with BOLD could result from differences in the venous contributions to the signal. The results suggest that perfusion methods may provide more accurate timing information of neuronal activities than BOLD-based imaging.

Estimation of the local statistical noise in positron emission tomography revisited: practical implementation.

The purpose of this report is to implement novel modifications to overcome the limitations of an existing algorithm for estimating the local statistical noise in a positron emission tomography (PET) image without performing repeated measures. The original algorithm is based on a modification of the filtered back-projection algorithm that allows the variance to be estimated using only a single sinogram. In addition, the effects of photon absorption, random coincidences, radioactive decay, and detector nonuniformity are taken into account. However, there are some limitations when applying this method with modern scanners. In particular, it is common practice to interleave the projections in the sinogram (to increase the sampling rate along each projection) and to perform an interpolation when actually back-projecting to reconstruct the images. Both of these procedures introduce covariance among the elements of the projections, which is cumbersome and impractical to deal with using the existing technique for creating a variance image. An alternative image reconstruction scheme that is shown to be equivalent to image reconstruction using traditional filtered back-projection greatly simplifies the estimation of the variance image. The proposed methods were tested by Monte Carlo simulations and by using repeated scans of a uniform phantom filled with F-18. Results demonstrate that the proposed methods are very rigorous and stable when compared to calculations of the local variance using either repeated measures with a large number of measurements, or region-of-interest estimates of the variance, assuming homogeneous variance structure. In addition, strategies for extending the proposed technique are discussed that would permit the estimation of the variance due to measurement error of a pixel in a brain map from both single subjects and pooled group data.