Proneness to social anxiety modulates neural complexity in the absence of exposure: A resting state fMRI study using Hurst exponent

Increased regional Hurst exponent reflects response inhibition related neural complexity alterations in pediatric bipolar disorder patients during an emotional Go-Nogo task

Fractal patterns have been shown to change in resting- and task-state blood oxygen level-dependent signals in bipolar disorder patients. However, fractal characteristics of brain blood oxygen level-dependent signals when responding to external emotional stimuli in pediatric bipolar disorder remain unclear. Blood oxygen level-dependent signals of 20 PBD-I patients and 17 age- and sex-matched healthy controls were extracted while performing an emotional Go-Nogo task. Neural responses relevant to the task and Hurst exponent of the blood oxygen level-dependent signals were assessed. Correlations between clinical indices and Hurst exponent were estimated. Significantly increased activations were found in regions covering the frontal lobe, parietal lobe, temporal lobe, insula, and subcortical nuclei in PBD-I patients compared to healthy controls in contrast of emotional versus neutral distractors. PBD-I patients exhibited higher Hurst exponent in regions that involved in action control, such as superior frontal gyrus, inferior frontal gyrus, inferior temporal gyrus, and insula, with Hurst exponent of frontal orbital gyrus correlated with onset age. The present study exhibited overactivation, increased self-similarity and decreased complexity in cortical regions during emotional Go-Nogo task in patients relative to healthy controls, which provides evidence of an altered emotional modulation of cognitive control in pediatric bipolar disorder patients. Hurst exponent may be a fractal biomarker of neural activity in pediatric bipolar disorder.

Advances in Understanding Fractals in Affective and Anxiety Disorders

In this chapter, we review the research that has applied fractal measures to the study of the most common psychological disorders, that is, affective and anxiety disorders. Early studies focused on heart rate, but diverse measures have also been examined, from variations in subjective mood, or hand movements, to electroencephalogram or magnetoencephalogram data. In general, abnormal fractal dynamics in different physiological and behavioural outcomes have been observed in mental disorders. Despite the disparity of variables measured, fractal analysis has shown high sensitivity in discriminating patients from healthy controls. However, and because of this heterogeneity in measures, the results are not straightforward, and more studies are needed in this promising line.

Fractal Time Series: Background, Estimation Methods, and Performances

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Pre-COVID resting-state brain activity in the fusiform gyrus prospectively predicts social anxiety alterations during the pandemic

BACKGROUND

Social anxiety (SA) has been linked to the coronavirus disease 2019 (COVID-19) pandemic, but the neurobiopsychological mechanisms underlying this relationship remain unclear. This study aimed to elucidate the neurofunctional markers for COVID-induced SA development and the potential role of COVID-related posttraumatic stress symptoms (PTSS) in the brain-SA alterations link.

METHODS

Before the COVID-19 pandemic (T1), 100 general college students underwent resting-state magnetic resonance imaging and behavioral tests. During the period of community-level outbreaks (T2), these students were re-contacted to undergo follow-up behavioral assessments.

RESULTS

Whole-brain correlation and prediction analyses found that pre-pandemic spontaneous neural activity (measured by fractional amplitude of low-frequency fluctuations) in the right fusiform gyrus (FG) was positively correlated to SA alterations (T2 - T1). Mediation analyses revealed that COVID-specific PTSS mediated the effects of right FG on SA alterations.

LIMITATIONS

The results should be interpreted carefully because only one-session neuroimaging data in a sample of normal adults were included.

CONCLUSIONS

The results provide evidence for neurofunctional markers of COVID-induced SA and may help develop targeted brain-based interventions that reduce SA.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression

As a major contributor to the development of depression, rumination has proven linked with aberrant default-mode network (DMN) activity. However, it remains unclear how the spontaneous spatial and temporal activity of DMN underlie the association between rumination and depression. To illustrate this issue, behavioral measures and resting-state functional magnetic resonance images were connected in 2 independent samples (NSample1 = 100, NSample2 = 95). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) were used to assess spatial characteristic patterns, while voxel-wise functional concordance (across time windows) (VC) and Hurst exponent (HE) were used to assess temporal dynamic patterns of brain activity. Results from both samples consistently show that temporal dynamics but not spatial patterns of DMN are associated with rumination. Specifically, rumination is positively correlated with HE and VC (but not fALFF and ReHo) values, reflecting more consistent and regular temporal dynamic patterns in DMN. Moreover, subregion analyses indicate that temporal dynamics of the ventromedial prefrontal cortex (VMPFC) reliably predict rumination scores. Furthermore, mediation analyses show that HE and VC of VMPFC mediate the association between rumination and depression. These findings shed light on neural mechanisms of individual differences in rumination and corresponding risk for depression.

Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features

Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown potential for predicting certain phenotypes or disorders from functional magnetic resonance images. In this study, we aimed to predict the level of social anxiety in young adult participants by training machine learning models with resting-state brain functional radiomic features including the regional homogeneity, fractional amplitude of low-frequency fluctuation, fractional resting-state physiological fluctuation amplitude, and degree centrality. Among the machine learning models, the XGBoost model achieved the best performance with balanced accuracy of 77.7% and F1 score of 0.815. Analysis of input feature importance demonstrated that the orbitofrontal cortex and the degree centrality were most relevant to predicting the level of social anxiety among the input brain regions and the input type of radiomic features, respectively. These results suggest potential validity for predicting social anxiety with machine learning of the resting-state brain functional radiomic features and provide further understanding of the neural basis of the symptom.

A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment

Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard–Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). Results: Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. Conclusion: The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.

Subcortical control of the default mode network: Role of the basal forebrain and implications for neuropsychiatric disorders

The precise interplay between large-scale functional neural systems throughout the brain is essential for performance of cognitive processes. In this review we focus on the default mode network (DMN), one such functional network that is active during periods of quiet wakefulness and believed to be involved in introspection and planning. Abnormalities in DMN functional connectivity and activation appear across many neuropsychiatric disorders, including schizophrenia. Recent evidence suggests subcortical regions including the basal forebrain are functionally and structurally important for regulation of DMN activity. Within the basal forebrain, subregions like the ventral pallidum may influence DMN activity and the nucleus basalis of Meynert can inhibit switching between brain networks. Interactions between DMN and other functional networks including the medial frontoparietal network (default), lateral frontoparietal network (control), midcingulo-insular network (salience), and dorsal frontoparietal network (attention) are also discussed in the context of neuropsychiatric disorders. Several subtypes of basal forebrain neurons have been identified including basal forebrain parvalbumin-containing or somatostatin-containing neurons which can regulate cortical gamma band oscillations and DMN-like behaviors, and basal forebrain cholinergic neurons which might gate access to sensory information during reinforcement learning. In this review, we explore this evidence, discuss the clinical implications on neuropsychiatric disorders, and compare neuroanatomy in the human vs rodent DMN. Finally, we address technological advancements which could help provide a more complete understanding of modulation of DMN function and describe newly identified BF therapeutic targets that could potentially help restore DMN-associated functional deficits in patients with a variety of neuropsychiatric disorders.

Monofractal analysis of functional magnetic resonance imaging: An introductory review

The following review will aid readers in providing an overview of scale-free dynamics and monofractal analysis, as well as its applications and potential in functional magnetic resonance imaging (fMRI) neuroscience and clinical research. Like natural phenomena such as the growth of a tree or crashing ocean waves, the brain expresses scale-invariant, or fractal, patterns in neural signals that can be measured. While neural phenomena may represent both monofractal and multifractal processes and can be quantified with many different interrelated parameters, this review will focus on monofractal analysis using the Hurst exponent (H). Monofractal analysis of fMRI data is an advanced analysis technique that measures the complexity of brain signaling by quantifying its degree of scale-invariance. As such, the H value of the blood oxygenation level-dependent (BOLD) signal specifies how the degree of correlation in the signal may mediate brain functions. This review presents a brief overview of the theory of fMRI monofractal analysis followed by notable findings in the field. Through highlighting the advantages and challenges of the technique, the article provides insight into how to best conduct fMRI fractal analysis and properly interpret the findings with physiological relevance. Furthermore, we identify the future directions necessary for its progression towards impactful functional neuroscience discoveries and widespread clinical use. Ultimately, this presenting review aims to build a foundation of knowledge among readers to facilitate greater understanding, discussion, and use of this unique yet powerful imaging analysis technique.

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.

Brain circuitry underlying the ABC model of anxiety

Anxiety Disorders are prevalent and often chronic, recurrent conditions that reduce quality of life. The first-line treatments, such as serotonin reuptake inhibitors and cognitive behavioral therapy, leave a significant proportion of patients symptomatic. As psychiatry moves toward targeted circuit-based treatments, there is a need for a theory that unites the phenomenology of anxiety with its underlying neural circuits. The Alarm, Belief, Coping (ABC) theory of anxiety describes how the neural circuits associated with anxiety interact with each other and domains of the anxiety symptoms, both temporally and spatially. The latest advancements in neuroimaging techniques offer the ability to assess these circuits in vivo. Using Neurosynth, a large open-access meta-analytic imaging database, the association between terms related to specific neural circuits was explored within the ABC theory framework. Alarm-related terms were associated with the amygdala, anterior cingulum, insula, and bed nucleus of stria terminalis. Belief-related terms were associated with medial prefrontal cortex, precuneus, bilateral temporal poles, and hippocampus. Coping-related terms were associated with the ventrolateral and dorsolateral prefrontal cortices, basal ganglia, and anterior cingulate. Neural connections underlying the functional neuroanatomy of the ABC model were observed. Additionally, there was considerable interaction and overlap between circuits associated with the symptom domains. Further neuroimaging research is needed to explore the dynamic interaction between the functional domains of the ABC theory. This will pave the way for probing the neuroanatomical underpinnings of anxiety disorders and provide an evidence-based foundation for the development of targeted treatments, such as neuromodulation.

Reductions in perceived stress following Transcendental Meditation practice are associated with increased brain regional connectivity at rest

Transcendental Meditation (TM) is defined as a mental process of transcending using a silent mantra. Previous work showed that relatively brief period of TM practice leads to decreases in stress and anxiety. However, whether these changes are subserved by specific morpho-functional brain modifications (as observed in other meditation techniques) is still unclear. Using a longitudinal design, we combined psychometric questionnaires, structural and resting-state functional magnetic resonance imaging (RS-fMRI) to investigate the potential brain modifications underlying the psychological effects of TM. The final sample included 19 naïve subjects instructed to complete two daily 20-min TM sessions, and 15 volunteers in the control group. Both groups were evaluated at recruitment (T0) and after 3 months (T1). At T1, only meditators showed a decrease in perceived anxiety and stress (t₍₁₈₎ = 2.53, p = 0.02), which correlated negatively with T1-T0 changes in functional connectivity among posterior cingulate cortex (PCC), precuneus and left superior parietal lobule. Additionally, TM practice was associated with increased connectivity between PCC and right insula, likely reflecting changes in interoceptive awareness. No structural changes were observed in meditators or control subjects. These preliminary findings indicate that beneficial effects of TM may be mediated by functional brain changes that take place after a short practice period of 3 months.

Wavelet p-Leader Non-Gaussian Multiscale Expansions for EEG series: an Exploratory Study on Cold-Pressor Test

Brain dynamics recorded through electroencephalography (EEG) have been proven to be the output of a nonstationary and nonlinear system. Thus, multifractality of EEG series has been exploited as a useful tool for a neurophysiological characterization in health and disease. However, the role of EEG multifractality under peripheral stress is unknown. In this study, we propose to make use of a novel tool, the recently defined non-Gaussian multiscale analysis, to investigate brain dynamics in the range of 4-8Hz following a cold-pressor test versus a resting state. The method builds on the wavelet p-leader multifractal spectrum to quantify different types of departure from Gaussian and linear properties, and is compared here to standard linear descriptive indices. Results suggest that the proposed non-Gaussian multiscale indices were able to detect expected changes over the somatosensory and premotor cortices, over regions different from those detected by linear analyses. They further indicate that preferred responses for the contralateral somatosensory cortex occur at scales 2.5s and 5s. These findings contribute to the characterization of the so-called central autonomic network, linking dynamical changes at a peripheral and a central nervous system levels.

Temporal dynamics of spontaneous default-mode network activity mediate the association between reappraisal and depression

Cognitive reappraisal is associated with major depressive disorder (MDD), while spontaneous activity patterns of the default mode network (DMN) is implicated in reappraisal and MDD. However, neural mechanisms subserving the close association of spontaneous reappraisal and depression are unclear. Spontaneous reappraisal, depression and resting-state functional magnetic resonance imaging (rsfMRI) were measured from 105 healthy subjects. We assessed the temporal complexity (Hurst exponent), Regional Homogeneity (ReHo) and fractional Amplitude of Low Frequency Fluctuation (fALFF) profiles of DMN, a network involved in both reappraisal and depression. Mediation effects of these standard measures on the relationship between reappraisal and depression, and the contributions of each DMN subregion, were assessed. Results indicated that Hurst exponent (H) of DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with reappraisal scores. An individual with a higher reappraisal score has a lower Hurst value of DMN. Mediation analyses suggest that H of DMN partially mediates the association between reappraisal and the degree of depression, and this mediation effect arises from the contribution of medial prefrontal cortex. Neither ReHo nor fALFF showed a similar correlation or mediation effect. These findings suggest that temporal dynamics of DMN play an important role in emotion regulation and its association with depression. H of DMN may serve as a neural marker mediating the association between reappraisal and depression.

Extrinsic and default mode networks in psychiatric conditions: Relationship to excitatory-inhibitory transmitter balance and early trauma

Over the last three decades there has been an accumulation of Magnetic Resonance Imaging (MRI) studies reporting that aberrant functional networks may underlie cognitive deficits and other symptoms across a range of psychiatric diagnoses. The use of pharmacological MRI and ¹H-Magnetic Resonance Spectroscopy (¹H-MRS) has allowed researchers to investigate how changes in network dynamics are related to perturbed excitatory and inhibitory neurotransmission in individuals with psychiatric conditions. More recently, changes in functional network dynamics and excitatory/inhibitory (E/I) neurotransmission have been linked to early childhood trauma, a major antecedents for psychiatric illness in adulthood. Here we review studies investigating whether perturbed network dynamics seen across psychiatric conditions are related to changes in E/I neurotransmission, and whether such changes could be linked to childhood trauma. Whilst there is currently a paucity of studies relating early traumatic experiences to altered E/I balance and network function, the research discussed here lead towards a plausible mechanistic hypothesis, linking early traumatic experiences to cognitive dysfunction and symptoms mediated by E/I neurotransmitter imbalances.

Fractal Analysis of BOLD Time Series in a Network Associated With Waiting Impulsivity

Fractal phenomena can be found in numerous scientific areas including neuroscience. Fractals are structures, in which the whole has the same shape as its parts. A specific structure known as pink noise (also called fractal or 1/f noise) is one key fractal manifestation, exhibits both stability and adaptability, and can be addressed via the Hurst exponent (H). FMRI studies using H on regional fMRI time courses used fractality as an important characteristic to unravel neural networks from artificial noise. In this fMRI-study, we examined 103 healthy male students at rest and while performing the 5-choice serial reaction time task. We addressed fractality in a network associated with waiting impulsivity using the adaptive fractal analysis (AFA) approach to determine H. We revealed the fractal nature of the impulsivity network. Furthermore, fractality was influenced by individual impulsivity in terms of decreasing fractality with higher impulsivity in regions of top-down control (left middle frontal gyrus) as well as reward processing (nucleus accumbens and anterior cingulate cortex). We conclude that fractality as determined via H is a promising marker to quantify deviations in network functions at an early stage and, thus, to be able to inform preventive interventions before the manifestation of a disorder.

A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent

Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimer’s disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia.

Precuneus-related regional and network functional deficits in social anxiety disorder: A resting-state functional MRI study

BACKGROUND

Neuroimaging findings suggest that social anxiety disorder (SAD) may be correlated with changes in regional- or network-level brain function. However, few studies have explored alterations in intrinsic resting cerebral function in patients with SAD at both the regional and network levels, particularly focusing on the theory of mind (ToM)-related regions. This study was performed to investigate changes in neural activity and functional connectivity (FC) in ToM-related regions during the resting state in SAD patients and to determine how these alterations are correlated with the clinical symptoms of SAD.

METHODS

Forty-three SAD patients and 43 matched healthy controls underwent resting-state functional magnetic resonance imaging (rsfMRI) scans. First, the amplitude of low-frequency fluctuation (ALFF) approach was used to explore regional activity. Then, the ToM-related region, i.e., the left precuneus, which showed altered ALFF values, was adopted as a seed for further FC analyses to assess network-level alterations in SAD. Between-group differences were compared using voxel-based two-sample t-tests (P<0.05, with Gaussian random field correction). Pearson's correlation analyses were performed to examine relationships between alterations in ALFF and FC and clinical symptoms.

RESULTS

Compared with the healthy controls, SAD patients showed decreased ALFF in the bilateral putamen (PUT) and left supplementary motor area (SMA) and increased ALFF in the right inferior parietal lobule (IPL), left precuneus and right cerebellar posterior lobe. Moreover, SAD patients exhibited lower connectivity between the left precuneus and the cerebellar posterior lobe, right inferior temporal gyrus (ITG), right parahippocampal gyrus (PHG) and left medial prefrontal cortex (mPFC). The altered ALFF values in the left precuneus and the hypoconnectivity between the left precuneus and left cerebellar posterior lobe were correlated with the patients' clinical symptoms (P<0.05).

CONCLUSION

The precuneus, a ToM-related region, was altered at both the regional and network level in patients with SAD. Pathological fear and avoidance in SAD were correlated with abnormal regional function in the precuneus, whereas depression and anxiety were primarily correlated with functional deficits in the precuneus-related network. The altered FC within the precuneus-cerebellar region may reflect an imbalance in the neuromodulation of anxiety and depressive symptoms in SAD. These findings may facilitate a greater understanding of potential SAD neural substrates and could be used to identify potential targets for further treatment.

Resting-State Functional Connectivity in Generalized Anxiety Disorder and Social Anxiety Disorder: Evidence for a Dimensional Approach

Generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are currently considered distinct diagnostic categories. Accumulating data suggest the study of anxiety disorders may benefit from the use of dimensional conceptualizations. One such dimension of shared dysfunction is emotion regulation (ER). The current study evaluated dimensional (ER) and categorical (diagnosis) neurocorrelates of resting-state functional connectivity (rsFC) in participants with GAD and SAD and healthy controls (HC). Functional magnetic resonance imaging (fMRI) rsFC was estimated between all regions of the default mode network (DMN), salience network (SN), and bilateral amygdala (N = 37: HC-19; GAD-10; SAD-8). Thereafter, rsFC was predicted by both ER, (using the Difficulties in Emotion Regulation Scale [DERS]), and diagnosis (DSM-5) within a single unified analysis of covariance (ANCOVA). For the ER dimension, there was a significant association between impaired ER abilities and anticorrelated rsFC of amygdala and DMN (L.amygdala-ACC: p = 0.011, beta = -0.345), as well as amygdala and SN (L.amygdala-posterior cingulate cortex [PCC]: p = 0.032, beta = -0.409). Diagnostic status was significantly associated with rsFC differences between the SAD and HC groups, both within the DMN (PCC-MPFC: p = 0.009) and between the DMN and SN (R.LP-ACC: p = 0.010). Although preliminary, our results exemplify the potential contribution of the dimensional approach to the study of GAD and SAD and support a combined categorical and dimensional model of rsFC of anxiety disorders.

Altered regional and integrated resting-state brain activity in general social anxiety disorder patients before and after group cognitive behavior therapy

We aimed to investigate the recovery neuromechanism underlying the treatment efficacy in generalized social anxiety disorder (GSAD). We recruited fifteen patients with GSAD and nineteen healthy control (HC) participants, all of whom underwent a baseline resting-state fMRI scan. The GSAD patients underwent an additional fMRI scan after group cognitive behavior therapy (GCBT). Amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) measures were used to examine altered regional and integrated spontaneous brain activity in group comparisons. After GCBT, ALFF of the right precuneus decreased. At baseline, the GSAD group showed higher ALFF in the left precuneus and the left middle temporal gyrus (MTG) and lower ALFF in the lingual gyrus compared with the HC group. The DC of the left precuneus and the MTG were attenuated and the right putamen increased in the post-treatment group. The changes in DC in the precuneus were positively correlated with changes in clinical symptom. The abnormal ALFF of the precuneus, MTG and lingual gyrus may be the neural underpinning of GSAD, whereas the neural response to symptom remission after GCBT was achieved by a rebalance within the default mode network.

Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process.

Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases

Major depressive disorder (MDD) is a leading world-wide psychiatric disorder with high recurrence rate, therefore, it is desirable to identify current MDD (cMDD) and remitted MDD (rMDD) for their appropriate therapeutic interventions. In the study, 19 cMDD, 19 rMDD and 19 well-matched healthy controls (HC) were enrolled and scanned with the resting-state functional magnetic resonance imaging (rs-fMRI). The Hurst exponent (HE) of rs-fMRI in AAL-90 and AAL-1024 atlases were calculated and compared between groups. Then, a radial basis function (RBF) based support vector machine was proposed to identify every pair of the cMDD, rMDD and HC groups using the abnormal HE features, and a leave-one-out cross-validation was used to evaluate the classification performance. Applying the proposed method with AAL-1024 and AAL-90 atlas respectively, 87% and 84% subjects were correctly identified between cMDD and HC, 84% and 71% between rMDD and HC, and 89% and 74% between cMDD and rMDD. Our results indicated that the HE was an effective feature to distinguish cMDD and rMDD from HC, and the recognition performances with AAL-1024 parcellation were better than that with the conventional AAL-90 parcellation.

Not in one metric: Neuroticism modulates different resting state metrics within distinctive brain regions

INTRODUCTION

Neuroticism is a complex personality trait encompassing diverse aspects. Notably, high levels of neuroticism are related to the onset of psychiatric conditions, including anxiety and mood disorders. Personality traits are stable individual features; therefore, they can be expected to be associated with stable neurobiological features, including the Brain Resting State (RS) activity as measured by fMRI. Several metrics have been used to describe RS properties, yielding rather inconsistent results. This inconsistency could be due to the fact that different metrics portray different RS signal properties and that these properties may be differently affected by neuroticism. To explore the distinct effects of neuroticism, we assessed several distinct metrics portraying different RS properties within the same population.

METHOD

Neuroticism was measured in 31 healthy subjects using the Zuckerman-Kuhlman Personality Questionnaire; RS was acquired by high-resolution fMRI. Using linear regression, we examined the modulatory effects of neuroticism on RS activity, as quantified by the Amplitude of low frequency fluctuations (ALFF, fALFF), regional homogeneity (REHO), Hurst Exponent (H), global connectivity (GC) and amygdalae functional connectivity.

RESULTS

Neuroticism modulated the different metrics across a wide network of brain regions, including emotional regulatory, default mode and visual networks. Except for some similarities in key brain regions for emotional expression and regulation, neuroticism affected different metrics in different ways.

DISCUSSION

Metrics more related to the measurement of regional intrinsic brain activity (fALFF, ALFF and REHO), or that provide a parsimonious index of integrated and segregated brain activity (HE), were more broadly modulated in regions related to emotions and their regulation. Metrics related to connectivity were modulated across a wider network of areas. Overall, these results show that neuroticism affects distinct aspects of brain resting state activity. More in general, these findings indicate that a multiparametric approach may be required to obtain a more detailed characterization of the neural underpinnings of a given psychological trait.

Brain activation to task-irrelevant disorder-related threat in social anxiety disorder: The impact of symptom severity

Unintentional and uncontrollable processing of threat has been suggested to contribute to the pathology of social anxiety disorder (SAD). The present study investigated the neural correlates of processing task-irrelevant, highly ecologically valid, disorder-related stimuli as a function of symptom severity in SAD. Twenty-four SAD patients and 24 healthy controls (HC) performed a feature-based comparison task during functional magnetic resonance imaging, while task-irrelevant, disorder-related or neutral scenes were presented simultaneously at a different spatial position. SAD patients showed greater activity than HC in response to disorder-related versus neutral scenes in brain regions associated with self-referential processing (e.g. insula, precuneus, dorsomedial prefrontal cortex) and emotion regulation (e.g. dorsolateral prefrontal cortex (dlPFC), inferior frontal gyrus). Symptom severity was positively associated with amygdala activity, and negatively with activation in dorsal anterior cingulate cortex and dlPFC in SAD patients. Additional correlation analysis revealed that amygdala-prefrontal coupling was positively associated with symptom severity. A network of brain regions is thus involved in SAD patients' processing of task-irrelevant, complex, ecologically valid, disorder-related scenes. Furthermore, increasing symptom severity in SAD patients seems to reflect a growing imbalance between neural mechanisms related to stimulus-driven bottom-up and regulatory top-down processes resulting in dysfunctional regulation strategies.

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI.

Abnormal brain activation and connectivity to standardized disorder-related visual scenes in social anxiety disorder

Our understanding of altered emotional processing in social anxiety disorder (SAD) is hampered by a heterogeneity of findings, which is probably due to the vastly different methods and materials used so far. This is why the present functional magnetic resonance imaging (fMRI) study investigated immediate disorder-related threat processing in 30 SAD patients and 30 healthy controls (HC) with a novel, standardized set of highly ecologically valid, disorder-related complex visual scenes. SAD patients rated disorder-related as compared with neutral scenes as more unpleasant, arousing and anxiety-inducing than HC. On the neural level, disorder-related as compared with neutral scenes evoked differential responses in SAD patients in a widespread emotion processing network including (para-)limbic structures (e.g. amygdala, insula, thalamus, globus pallidus) and cortical regions (e.g. dorsomedial prefrontal cortex (dmPFC), posterior cingulate cortex (PCC), and precuneus). Functional connectivity analysis yielded an altered interplay between PCC/precuneus and paralimbic (insula) as well as cortical regions (dmPFC, precuneus) in SAD patients, which emphasizes a central role for PCC/precuneus in disorder-related scene processing. Hyperconnectivity of globus pallidus with amygdala, anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC) additionally underlines the relevance of this region in socially anxious threat processing. Our findings stress the importance of specific disorder-related stimuli for the investigation of altered emotion processing in SAD. Disorder-related threat processing in SAD reveals anomalies at multiple stages of emotion processing which may be linked to increased anxiety and to dysfunctionally elevated levels of self-referential processing reported in previous studies.

Fear of negative evaluation is associated with altered brain function in nonclinical subjects

Social anxiety disorder (SAD), which involves excessive anxiety and fear of negative evaluation, is accompanied by abnormalities in brain function. While social anxiety appears to be represented on a spectrum ranging from nonclinical behavior to clinical manifestation, neural alteration in nonclinical populations remains unclear. This study examined the relationship between psychological measures of social anxiety, mainly using the Fear of Negative Evaluation Scale (FNES), and brain function (functional connectivity, degree centrality, and regional betweenness centrality). Results showed that FNES scores and functional connectivity of the parahippocampal gyrus and orbitofrontal cortex and the betweenness centrality of the right parietal cortex were negatively correlated. These regions are altered in SAD patients, and each is associated with social cognition and emotional processing. The results supported the perspective that social anxiety occurs on a spectrum and indicated that the FNES is a useful means of detecting neural alterations that may relate to the social anxiety spectrum. In addition, the findings indicated that graph analysis was useful in investigating the neural underpinnings of SAD in addition to other psychiatric symptoms.

Beyond emotions: A meta-analysis of neural response within face processing system in social anxiety

Patients with social anxiety disorder (SAD) experience anxiety and avoidance in face-to-face interactions. We performed a meta-analysis of functional magnetic resonance imaging (fMRI) studies in SAD to provide a comprehensive understanding of the neural underpinnings of face perception in this disorder. To this purpose, we adopted an innovative approach, asking authors for unpublished data. This is a common procedure for behavioral meta-analyses, which, however has never been used in neuroimaging studies. We searched Pubmed with the key words "Social Anxiety AND faces" and "Social Phobia AND faces." Then, we selected those fMRI studies for which we were able to obtain data for the comparison between SAD and healthy controls (HC) in a face perception task, either from the published papers or from the authors themselves. In this way, we obtained 23 studies (totaling 449 SAD and 424 HC individuals). We identified significant clusters in which faces evoked a higher response in SAD in bilateral amygdala, globus pallidus, superior temporal sulcus, visual cortex, and prefrontal cortex. We also found a higher activity for HC in the lingual gyrus and in the posterior cingulate. Our findings show that altered neural response to face in SAD is not limited to emotional structures but involves a complex network. These results may have implications for the understanding of SAD pathophysiology, as they suggest that a dysfunctional face perception process may bias patient person-to-person interactions.