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Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences in connectivity and activation seen in major depressive disorder (MDD) patients with and without suicidal ideation and the control group. For our investigation, a brain atlas containing 116 regions of interest was used. We also used four voxel-based connectivity models, including degree centrality, the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity, and voxel-mirrored Homotopic Connectivity. Feature selection was conducted using a sequential backward floating selection approach along with a Random Forest Classifier and Elastic Net. While all four models yield significant results, fALFF demonstrated higher accuracy rates in classifying the three groups. Further analysis revealed three features that demonstrated statistically significant differences between these three, resulting in a 90.00% accuracy rate. Prominent features identified from our analysis, with suicide ideation as the key variable, included the Superior frontal gyrus (dorsolateral and orbital parts), the median cingulate, and the paracingulate gyri. These areas are associated with the Central Executive Control Network (ECN), the Default Mode Network, and the ECN, respectively. Comparing the results of MDD patients with suicidal ideation to those without suicidal ideations suggests dysfunctions in decision-making ability, in MDD females suffering from suicidal tendencies. This may be related to a lack of inhibition or emotion regulation capability, which contributes to suicidal ideations. [ABSTRACT FROM AUTHOR]

Purpose: Reporting confidence after a decision-making task is widely used in the studies of metacognition. A cognitive factor is usually defined as "thinking about thinking." When people predict others' behavior in risky situations, they consider various factors affecting others' choices; at that point, they can determine how confident they are about their predictions about the others' decision. Materials and Methods: This study investigates human neural activities in different confidence levels when participants predict others' financial choices in a risky decision-making task. For this aim, functional Magnetic Resonance Imaging (fMRI) combined with behavioral tasks is used to demonstrate the neural representation of human confidence level about others' possible choices. We scanned 21 healthy and normal participants in two separate sessions each containing three runs. Results: The results indicate that the Frontal Pole Cortex (FPC), cingulate gyrus, and precuneus cortex activities are correlated with the confidence of people in their predictions (P < 0.0005; cluster size, k > 75). Using behavioral data, we found that when participants answer correctly, their confidence level as a metacognition factor increases simultaneously and vice versa. Conclusion: These key findings suggest that the brain's activities can represent subjects' confidence level in predicting risky behaviors and show how metacognition in the theory of mind for prediction of others' choices is represented in the brain's activity. [ABSTRACT FROM AUTHOR]

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars, and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias toward the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for the behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention. [ABSTRACT FROM AUTHOR]

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we examined the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli. Focusing on the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that although the weighted sum and weighted average models also made acceptable predictions in some conditions, the response to multiple stimuli could generally be better described by a model that takes normalization into account. We then determined the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide evidence that the normalization model can predict responses to objects across shifts of visual attention, suggesting the role of normalization as a fundamental operation in the human brain. [ABSTRACT FROM AUTHOR]

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data. Index Terms--Adaptive spatial filtering, fMRI activation detection, permutation, signal subspace.

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods. Index Terms--Functional magnetic resonance imaging (fMRI), randomization, signal detection, translation invariant wavelet transform.