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Autism spectrum disorder (ASD) is a behaviorally defined complex neurodevelopmental syndrome characterized by persistent social communication and interaction deficit. Transcranial magnetic stimulation (TMS) is a promising and emerging tool for the intervention of ASD by reducing both core and associate symptoms. Several reviews have been published regarding TMS-based ASD treatment, however, a systematic review on study characteristics, specific stimulating parameters, localization techniques, stimulated targets, behavioral outcomes, and neuroimage biomarker changes is lagged behind since 2018. Here, we performed a systematic search on literatures published after 2018 in PubMed, Web of Science, and Science Direct. After screening, the final systematic review included 17 articles, composing seven randomized controlled trial studies and ten open-label studies. Two studies are double-blind, while the other studies have a moderate to high risk of bias attributing to inadequate subject- and evaluator-blinding to treatment allocation. Five studies utilize theta-burst stimulation mode, and the others apply repetitive TMS with low frequency (five studies), high frequency (six studies), and combined low and high frequency stimulation (one study). Most researchers prioritize the bilateral dorsolateral prefrontal lobe as stimulation target, while parietal lobule, inferior parietal lobule, and posterior superior temporal sulci have also emerged as new targets of attention. One third of the studies use neuronavigation based on anatomical magnetic resonance imaging to locate the stimulation target. After TMS intervention, discernible enhancements across a spectrum of scales are evident in stereotyped behavior, repetitive behavior, and verbal social domains. A comprehensive review of literature spanning the last five years demonstrates the potential of TMS treatment for ASD in ameliorating the clinical core symptoms.

Introduction: The coil handle orientation plays a pivotal role in the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS). However, there is currently no consensus on the optimal individualized coil handle orientation, especially for non-motor areas. Case presentation: The present case reported a short-term effect of functional connectivity (FC)-guided rTMS with coil handle posterior-anterior 45° (PA45°) and posterior-anterior 135° (PA135°) on a patient with insomnia. Notably, in this case, the PA45° orientation was nearly perpendicular to the adjacent sulcus, while the PA135° orientation was almost parallel to it. Local brain activity and functional connectivity were assessed using resting-state functional magnetic resonance imaging (RS-fMRI). Additionally, motor evoked potentials (MEPs) were captured both pre and post-rTMS sessions. Findings: The coil handle orientation PA45° outperformed the PA135° in both RS-fMRI and MEP outcomes. Moreover, a 9-day rTMS treatment led to discernible improvements in symptoms of depression and anxiety, complemented by a modest enhancement in sleep quality.

Abstract The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Background: High-frequency rTMS over the dorsolateral prefrontal cortex (DLPFC) has demonstrated mixed effects on chronic and provoked pain. Objectives/Methods: In this study, a meta-analysis was conducted to characterise the potential analgesic effects of high-frequency rTMS over the DLPFC on both chronic and provoked pain. Results: A total of 626 studies were identified in a systematic search. Twenty-six eligible studies were included for the quantitative review, among which 17 modulated chronic pain and the remaining investigated the influence on provoked pain. The left side DLPFC was uniformly targeted in the chronic pain studies. While our data identified no overall effect of TMS across chronic pain conditions, there was a significant short-term analgesia in neuropathic pain conditions only (SMD = −0.87). In terms of long-lasting analgesia, there was an overall pain reduction in the midterm (SMD = −0.53, 24.6 days average) and long term (SMD = −0.63, 3 months average) post DLPFC stimulation, although these effects were not observed within specific chronic pain conditions. Surprisingly, the number of sessions was demonstrated to have no impact on rTMS analgesia. In the analysis of provoked pain, our data also indicated a significant analgesic effect following HF-rTMS over the DLPFC (SMD = −0.73). Importantly, we identified a publication bias in the studies of provoked pain but not for chronic pain conditions. Conclusions: Overall, our findings support that HF-DLPFC stimulation is able to induce an analgesic effect in chronic pain and in response to provoked pain. These results highlight the potential of DLPFC-rTMS in the management of certain chronic pain conditions and future directions are discussed to enhance the potential long-term analgesic effects.

Most stroke repetitive transcranial magnetic stimulation (rTMS) studies have used hand motor hotspots as rTMS stimulation targets; in addition, recent studies demonstrated that functional magnetic resonance imaging (fMRI) task activation could be used to determine suitable targets due to its ability to reveal individualized precise and stronger functional connectivity with motor-related brain regions. However, rTMS is unlikely to elicit motor evoked potentials in the affected hemisphere, nor would activity be detected when stroke patients with severe hemiplegia perform an fMRI motor task using the affected limbs. The current study proposed that the peak voxel in the resting-state fMRI (RS-fMRI) motor network determined by independent component analysis (ICA) could be a potential stimulation target. Twenty-one healthy young subjects underwent RS-fMRI at three visits (V1 and V2 on a GE MR750 scanner and V3 on a Siemens Prisma) under eyes-open (EO) and eyes-closed (EC) conditions. Single-subject ICA with different total number of components (20, 30, and 40) were evaluated, and then the locations of peak voxels on the left and right sides of the sensorimotor network (SMN) were identified. While most ICA RS-fMRI studies have been carried out on the group level, that is, Group-ICA, the current study performed individual ICA because only the individual analysis could guide the individual target of rTMS. The intra- (test-retest) and inter-scanner reliabilities of the peak location were calculated. The use of 40 components resulted in the highest test-retest reliability of the peak location in both the left and right SMN compared with that determined when 20 and 30 components were used for both EC and EO conditions. ICA with 40 components might be another way to define a potential target in the SMN for poststroke rTMS treatment.

Summary: Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7–50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003–0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.

Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic.Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample.Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms.Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established.Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.

Volitional eye closure is observed only in conscious and awake humans, and is rare in animals. It is believed that eye closure can focus one's attention inward and facilitate activities such as meditation and mental imagery. Congenital blind individuals are also required to close their eyes for these activities. Resting-state functional magnetic resonance imaging (RS-fMRI) studies have found robust differences between the eyes-closed (EC) and eyes-open (EO) conditions in some brain regions in the sighted. This study analyzed data from 21 congenital blind individuals and 21 sighted controls by using amplitude of low-frequency fluctuation (ALFF) of RS-fMRI. The blind group and the sighted group shared similar pattern of differences between the EC and EO condition: ALFF was higher in the EC condition than the EO condition in the bilateral primary sensorimotor cortex, bilateral supplementary motor area, and inferior occipital cortex, while ALFF was lower in the EC condition than the EO condition in the medial prefrontal cortex, highlighting the “nature” effect on the difference between the EC and EO conditions. The results of other matrices such as fractional ALFF (fALFF) and regional homogeneity (ReHo) showed similar patterns to that of ALFF. Moreover, no significant difference was observed between the EC-EO pattern of the two subgroups of congenital blind (i.e., with and without light perception), suggesting that the EC-EO difference is irrespective of residual light perception which reinforced the “nature” effect. We also found between-group differences, i.e., more probably “nurture effect”, in the posterior insula and fusiform. Our results suggest that the acts of closing and opening the eyes are of importance for the congenital blind, and that these actions and their differences might be inherent in the nature of humans.

Anxious major depressive disorder is a common subtype of major depressive disorder; however, its unique neural mechanism is not well-understood currently. Using multimodal MRI data, this study examined common and specific alterations of amygdala subregions between patients with and without anxiety. No alterations were observed in the gray matter volume or intra-region functional integration in either patient group. Compared with the controls, both patient groups showed decreased functional connectivity between the left superficial amygdala and the left putamen, and between the right superficial amygdala and the bilateral anterior cingulate cortex and medial orbitofrontal cortex, while only patients with anxiety exhibited decreased activity in the bilateral laterobasal and superficial amygdala. Moreover, the decreased activity correlated negatively with the Hamilton depression scale scores in the patients with anxiety. These findings provided insights into the pathophysiologic processes of anxious major depressive disorder and may help to develop new and effective treatment programs.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with onset during childhood. Because of its complex spectrum of phenotypes, the underlying pathophysiology of TS is still unclear. Resting-state functional magnetic resonance imaging demonstrated aberrant spontaneous neural synchronization in conventional frequency band (0.01–0.08 Hz) in TS. No published studies have reported abnormalities of local synchronization across different frequency bands. We estimated the alterations of local synchronization across five bands ranging from 0 to 0.25 Hz. Seventy-nine children with TS and 63 age-, sex-, and handedness-matched healthy children were recruited. Frequency-specific regional homogeneity (ReHo) and independent component analysis were used to identify functional alterations between TS and healthy children. TS patients showed significantly increased ReHo in the left precentral gyrus and decreased ReHo in the right operculum. Abnormal ReHo alterations of the superior frontal gyrus, superior parietal gyrus, anterior cingulate gyrus, putamen, superior temporal gyrus, and operculum were observed in different frequency bands. TS patients showed increased connectivity of the right superior frontal gyrus within the left executive control network. In addition, a significantly negative correlation was found between Yale Global Tic Severity Scale (YGTSS) vocal score and ReHo values of the right operculum in the highest frequency bands (0.198–0.25 Hz), while a significant positive correlation was found between YGTSS motor score and altered connectivity of the right superior frontal gyrus. The present study revealed frequency-specific abnormal alterations of ReHo in the whole brain and altered connectivity within the executive control network of TS children. Its neural importance and clinical practicability require further investigation.

Reaction-time variability is a critical index of sustained attention. However, researchers still lack effective measures to establish the association between neurophysiological activity and this behavioral variability. Here, the present study recorded reaction time (RT) and cortical electroencephalogram (EEG) in healthy subjects when they continuously performed an alternative responding task. The frontal theta activity and reaction-time variability were examined trial by trial using the measures of standard deviation (SD) in the time domain and amplitude of low-frequency fluctuation (ALFF) in the frequency domain. Our results showed that the SD of reaction-time variability did not have any correlation with the SD of trial-by-trial frontal theta activity, and the ALFF of reaction-time variability has a significant correlation with the ALFF of trial-by-trial frontal theta activity in 0.01–0.027 Hz. These results suggested the methodological significance of ALFF in establishing the association between neurophysiological activity and reaction-time variability. Furthermore, these findings also support the low-frequency fluctuation as a potential feature of sustained attention.

Abstract Background The dorsolateral prefrontal cortex (DLPFC) is the standard stimulation target for the repetitive transcranial magnetic stimulation (rTMS) treatment of major depression disorder (MDD). A retrospective study by Fox and colleagues found that a more negative resting‐state functional magnetic resonance imaging (RS‐fMRI) functional connectivity (FC) between left DLPFC and the subgenual anterior cingulate cortex (sgACC) in a large group of healthy participants is associated with a better curative effects of rTMS in MDD, suggesting that the sgACC may be an effective region. However, a recent meta‐analysis on RS‐fMRI studies found that the pregenual ACC (pgACC), rather than the sgACC, of MDD patients showed increased local activity. Methods We used the stimulation coordinates in the left DLPFC analyzed by Fox et al. to perform RS‐fMRI FC between the stimulation targets obtained from previous rTMS MDD studies and the potential effective regions (sgACC and pgACC, respectively) on the RS‐fMRI data from 88 heathy participants. Results (a) Both the pgACC and the sgACC were negatively connected to the left DLPFC; (b) both FCs of sgACC‐DLPFC and pgACC‐DLPFC were more negative in responders than in nonresponders; and (c) the associations between DLPFC‐sgACC functional connectivity and clinical efficacy were clustered around the midline sgACC. Conclusions Both the pgACC and the sgACC may be potential effective regions for rTMS on the left DLPFC for treatment of MDD. However, individualized ACC‐DLPFC FC‐based rTMS on depression should be performed in the future to test the pgACC or the sgACC as effective regions.

The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). Wavelet transform (WT) is more effective in representing the complex waveform due to its adaptivity to non-stationary or local features of data and many varieties of wavelet functions with different shapes being available. However, there is a paucity of RS-fMRI studies that systematically compare between the results of FFT versus WT. The present study employed five cohorts of datasets and compared the sensitivity and reproducibility of FFT-ALFF with those of Wavelet-ALFF based on five mother wavelets (namely, db2, bior4.4, morl, meyr, and sym3). In addition to the conventional frequency band of 0.0117–0.0781 Hz, a comparison was performed in sub-bands, namely, Slow-6 (0–0.0117 Hz), Slow-5 (0.0117–0.0273 Hz), Slow-4 (0.0273–0.0742 Hz), Slow-3 (0.0742–0.1992 Hz), and Slow-2 (0.1992–0.25 Hz). The results indicated that the Wavelet-ALFF of all five mother wavelets was generally more sensitive and reproducible than FFT-ALFF in all frequency bands. Specifically, in the higher frequency band Slow-2 (0.1992–0.25 Hz), the mean sensitivity of db2-ALFF results was 1.54 times that of FFT-ALFF, and the reproducibility of db2-ALFF results was 2.95 times that of FFT-ALFF. The findings suggest that wavelet-ALFF can replace FFT-ALFF, especially in the higher frequency band. Future studies should test more mother wavelets on other RS-fMRI metrics and multiple datasets.

Functional magnetic resonance imaging (fMRI) studies have shown that the effect of repetitive transcranial magnetic stimulation (rTMS) can induce changes in remote brain regions. In the stimulated regions, low-frequency (≤1 Hz) rTMS induces inhibitory effects, while high-frequency (≥5 Hz) stimulation induces excitatory effects. However, these stereotypical effects arising from low- and high-frequency stimulation are based on measurements of motor evoked potentials (MEPs) induced by pulsed stimulation. To test the effects of rTMS on remote brain regions, the current study recruited 31 young healthy adults who participated in three rTMS sessions (10 Hz high frequency, 1 Hz low frequency, and sham) on three separate days. The stimulation target was based on individual fMRI activation in the motor cortex evoked by a finger movement task. Pre- and post-rTMS resting-state fMRI (RS-fMRI) were acquired. Regional homogeneity (ReHo) and degree centrality (DC) were calculated to measure the local and global connectivity, respectively. Compared with the sham session, high-frequency (10 Hz) rTMS significantly increased ReHo and DC in the right cerebellum, while low-frequency (1 Hz) stimulation did not significantly alter ReHo or DC. Then, using a newly developed PAIR support vector machine (SVM) method, we achieved accuracy of 93.18–97.24% by split-half validation for pairwise comparisons between conditions for ReHo or DC. While the univariate analyses suggest that high-frequency rTMS of the left motor cortex could affect distant brain activity in the right cerebellum, the multivariate SVM results suggest that both high- and low-frequency rTMS significantly modulated widespread brain activity. The current findings are useful for increasing the understanding of the mechanisms of rTMS, as well as guiding precise individualized rTMS treatment of movement disorders.

Background: Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. Methods: The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. Results: Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. Conclusions: Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.

Many studies have shown abnormal functional connectivity in children with attention deficit hyperactivity disorder (ADHD) by using resting-state functional magnetic resonance imaging (rs-fMRI). However, few studies illustrated that to what extent these findings were consistent across different datasets. The present study aimed to assess the consistency of abnormal functional connectivity in children with ADHD across the four datasets from a public-assess rs-fMRI ADHD cohort, namely, ADHD-200. We employed the identical analysis process of previous studies and examined a few factors, including connectivity with the seed regions of the bilateral dorsal anterior cingulate cortex, bilateral inferior frontal gyrus, and bilateral middle frontal gyrus; connectivity between default mode network and executive control network; stringent and lenient statistical thresholds; and the ADHD subtypes. Our results revealed a high inconsistency of abnormal seed-based connectivity in children with ADHD across all datasets, even across three datasets from the same research site. This inconsistency could also be observed with a lenient statistical threshold. Besides, each dataset did not show abnormal connectivity between default mode network and executive control network for ADHD, albeit this abnormal connectivity between networks was intensively reported in previous studies. Importantly, the ADHD combined subtype showed greater consistency than did the inattention subtype. These findings provided methodological insights into the studies on spontaneous brain activity of ADHD, and the ADHD subtypes deserve more attention in future studies.

Sustained attention involves two distinct processes, i.e., external focus and internal focus. Some recent neuroimaging studies employed the instruction of experimenters or the self-report from participants to generate the two attentional processes, and observed that the default mode network (DMN) was also responding to the external focus. These observations challenged the general view that the DMN accounts for the internally directed cognition, e.g., unfocused mind wandering, task independent-thoughts and internally focused events. Notably, the instruction or self-report may not effectively ensure the participants engage in the external focus/internal focus, and thus, the functional significance of the DMN for the externally focused process remains to be verified. In the present study, a new task paradigm, i.e., real/sham continuous feedback of finger force, was employed to generate the attentional process of external focus/internal focus, and the functional connectivity among the node regions of the DMN was further investigated in the two processes respectively. We found that two regions of the DMN, posterior cingulate cortex and left inferior parietal cortex/angular gyrus showed stronger inter-regional connectivity in the externally focused process than it in the internally focused process. Intriguingly, this functional connectivity was closely related to the behavioral performance in the process of external focus. These findings implicated that the functional significance of the DMN in sustained attention was more than responding to the internally directed cognition, and the task paradigm of continuous finger force feedback could benefit for the future studies on the externally focused/internally focused process of sustained attention.

Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal.

Objectives: Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data. Methods: We performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into regression models to investigate the relationships between diagnosis, symptom severity, gender, and age. Results: Compared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Age-related growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices. Conclusions: Through a multimodal analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 – V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account.

Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing—increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.

Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies.

Beneficial effects of repetitive transcranial magnetic stimulation (rTMS) on left dorsolateral prefrontal cortex (DLPFC) have been consistently shown for treating various neuropsychiatrical or neuropsychological disorders, but relatively little is known about its neural mechanisms. Here we conducted a randomized, double-blind, SHAM-controlled study to assess the effects of high-frequency left DLPFC rTMS on resting-state activity. Thirty-eight young healthy subjects received two sessions of either real rTMS (N = 18, 90% motor-threshold; left DLPFC at 20 Hz) or SHAM TMS (N = 20) and functional magnetic resonance imaging scan during rest in 2 days separated by 48 h. Resting-state bran activity was measured with the fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC). Increased fALFF was found in rostral anterior cingulate cortex (rACC) after 20 Hz rTMS, while no changes were observed after SHAM stimulation. Using the suprathreshold rACC cluster as the seed, increased FC was found in left temporal cortex (stimulation vs. group interaction). These data suggest that high-frequency rTMS on left DLPFC enhances low-frequency resting-state brain activity in the target site and remote sites as reflected by fALFF and FC.

We aimed to elucidate the dACC laterality in typically developing children and their sex/age-related differences with a sample of 84 right-handed children (6–16 years, 42 boys). We first replicated the previous finding observed in adults that gray matter density asymmetry in the dACC was region-specific: leftward (left > right) in its superior part, rightward (left

Many papers have shown results from the multi-site dataset of resting-state fMRI (rs-fMRI) in attention deficit hyperactivity disorder (ADHD), a data-sharing project named ADHD-200. However, few studies have illustrated that to what extent the pooled findings were consistent across cohorts. The present study analyzed three voxel-wise whole-brain metrics, i.e., amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) based on the pooled dataset as well as individual cohort of ADHD-200. In addition to the conventional frequency band of 0.01–0.08 Hz, sub-frequency bands of 0–0.01, 0.01–0.027, 0.027–0.073, 0.073–0.198, and 0.198–0.25 Hz, were assessed. While the pooled dataset showed abnormal activity in some brain regions, e.g., the bilateral sensorimotor cortices, bilateral cerebellum, and the bilateral lingual gyrus, these results were highly inconsistent across cohorts, even across the three cohorts from the same research center. The standardized effect size was rather small. These findings suggested a high heterogeneity of spontaneous brain activity in ADHD. Future studies based on multi-site large-sample dataset should be performed on pooled data and single cohort data, respectively and the effect size must be shown.

Physical exercise can improve physical and mental health. A number of imaging studies have examined the role of neuroplasticity in improving cognition with physical exercise; however, such neuroplasticity changes are not consistent across the reports partly due to small sample sizes in some studies. We thought to explore the concept that identifying consistent findings across multi-modality imaging measures would provide relatively reliable results. We designed a 6-week quasi-experiment with Wii-fitness exercise program in 24 healthy adults older than 60, and then examined the changes on neuroimaging measures including brain volume, the amplitude of low-frequency oscillation function (ALFF), regional homogeneity (ReHo), seed-based functional connectivity (FC), and the global efficiency of nodal connectivity during resting state. We focused on whether there were common regions showing changes after exercise across these measures and which measure was closely correlated with cognitive improvement. After the six-week exercise program, participants demonstrated a significant improvement in memory and executive function on neuropsychological tests, and in memory recall on an emotional memory task. The common brain regions that showed significant changes across different measures were the right striatum and the posterior cingulate (PCC). After exercise, the PCC showed decreased ReHo and increased volume, and the striatum did not show volume loss as the control group did and increased its FC with the cingulate, temporal, parietal, and occipital regions. Moreover, the connectivity change between the striatum and the thalamus was correlated with the improvement of executive function. This result implicates the striatum and the PCC associated network in physical exercise. Our work highlights the effectiveness of multi-modality neuroimaging measures in investigating neuroplasticity.

Structural MRI (sMRI)-identified tissue "growth" after neuropsychological training has been reported in many studies but the origins of those apparent tissue changes (ATC) still remain elusive. One possible contributor to ATC is brain perfusion since T1-weighted MRI, the tool used to identify ATC, is sensitive to perfusion-change induced tissue T1 alterations. To test the hypothetical perfusion contribution to ATC, sMRI data were acquired before and after short-term global and regional perfusion manipulations via intaking a 200 mg caffeine pill and performing a sensorimotor task. Caffeine intake caused a global CBF reduction and apparent tissue density reduction in temporal cortex, anterior cingulate cortex, and the limbic area; sensorimotor task induced CBF increase and apparent tissue increase in spatially overlapped brain regions. After compensating CBF alterations through a voxel-wise regression, the ATC patterns demonstrated in both experiments were substantially suppressed. These data clearly proved existence of the perfusion contribution to short-term ATC, and suggested a need for correcting perfusion changes in longitudinal T1-weighted structural MRI analysis if a short-term design is used.

Increased levels of reaction time variability (RTV) are characteristics of sustained attention deficits. The clinical significance of RTV has been widely recognized. However, the reliability of RTV measurements has not been widely studied. The present study aimed to assess the test-retest reliability of RTV conventional measurements, e.g., the standard deviation (SD), the coefficient of variation (CV), and a new measurement called the amplitude of low frequency fluctuation (ALFF) of RT. In addition, we aimed to assess differences and similarities of these measurements between different tasks.Thirty-seven healthy college students participated in 2 tasks, i.e., an Eriksen flanker task (EFT) and a simple reaction task (SRT), twice over a mean interval of 56 days. Conventional measurements of RTV including RT-SD and RT-CV were assessed first. Then the RT time series were converted into frequency domains, and RT-ALFF was further calculated for the whole frequency band (0.0023-0.167 Hz) and for a few sub-frequency bands including Slow-6 (

Globus pallidus interna (GPi) is an effective deep brain stimulation site for the treatment of Tourette syndrome (TS), and plays a crucial role in the pathophysiology of TS. To investigate the functional network feature of GPi in TS patients, we retrospectively studied 24 boys with ‘pure’ TS and 32 age-/education-matched healthy boys by resting state functional magnetic resonance images. Amplitude of low-frequency fluctuation (ALFF) and functional connectivity were used to estimate the local activity in GPi and its functional coordinate with the whole brain regions, respectively. We found decreased ALFF in patients’ bilateral GPi, which was also negatively correlated with clinical symptoms. Functional connectivity analysis indicated abnormal regions within motor and limbic networks in patients (inferior part of sensorimotor area, cerebellum, prefrontal cortex, cingulate gyrus, caudate nucleus, medial temporal lobe, and brain stem). Transcranial magnetic stimulation sites defined by previous studies (hand knob area, premotor area, and supplementary motor area) did not show significantly different functional connectivity with GPi between groups. In summary, this study characterized the disrupted functional network of GPi and provided potential regions-of-interest for further basic and clinical studies on TS.

The amplitude of low-frequency fluctuation (ALFF) measures low-frequency oscillations of the blood-oxygen-level-dependent signal, characterizing local spontaneous activity during the resting state. ALFF is a commonly used measure for resting-state functional magnetic resonance imaging (rs-fMRI) in numerous basic and clinical neuroscience studies. Using a test-retest rs-fMRI dataset consisting of 21 healthy subjects and three repetitive scans, we found that several key brain regions with high ALFF intensities (or magnitude) had poor reliability. Such regions included the posterior cingulate cortex, the medial prefrontal cortex in the default mode network, parts of the right and left thalami, and the primary visual and motor cortices. The above finding was robust with regard to different sample sizes (number of subjects), different scanning parameters (repetition time) and variations of test-retest intervals (i.e., intra-scan, intra-session, and inter-session reliability), as well as with different scanners. Moreover, the qualitative, map-wise results were validated further with a region-of-interest-based quantitative analysis using "canonical" coordinates as reported previously. Therefore, we suggest that the reliability assessments be incorporated in future ALFF studies, especially for the brain regions with a large ALFF magnitude as listed in our paper. Splitting single data into several segments and assessing within-scan "test-retest" reliability is an acceptable alternative if no "real" test-retest datasets are available. Such evaluations might become more necessary if the data are collected with clinical scanners whose performance is not as good as those that are used for scientific research purposes and are better maintained because the lower signal-to-noise ratio may further dampen ALFF reliability.

The aim of the current study was to localize the epileptic focus and characterize its causal relation with other brain regions, to understand the cognitive deficits in children with benign childhood epilepsy with centrotemporal spikes (BECTS). Resting-state functional magnetic resonance imaging (fMRI) was performed in 37 children with BECTS and 25 children matched for age, sex and educational achievement. We identified the potential epileptogenic zone (EZ) by comparing the amplitude of low frequency fluctuation (ALFF) of spontaneous blood oxygenation level dependent fMRI signals between the groups. Granger causality analysis was applied to explore the causal effect between EZ and the whole brain. Compared with controls, children with BECTS had significantly increased ALFF in the right postcentral gyrus and bilateral calcarine, and decreased ALFF in the left anterior cingulate cortex, bilateral putaman/caudate, and left cerebellum. ALFF values in the putaman/caudate were positively correlated with verbal IQ scores in patients. The ALFF values in cerebellum and performance IQ scores were negatively correlated in patients. These results suggest that ALFF disturbances in the putaman/caudate and cerebellum play an important role in BECTS cognitive dysfunction. Compared with controls, the patients showed increased driving effect from the EZ to the right medial frontal cortex and posterior cingulate cortex and decreased causal effects from the EZ to left inferior frontal gyrus. The causal effect of the left inferior frontal gyrus negatively correlated with disease duration, which suggests a relation between the epileptiform activity and language impairment. All together, these findings provide additional insight into the neurophysiological mechanisms of epilepitogenisis and cognitive dysfunction associated with BECTS.

Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.

Resting-state fMRI studies have increasingly focused on multi-contrast techniques, such as BOLD and ASL imaging. However, these techniques may reveal different aspects of brain activity (e.g., static vs. dynamic), and little is known about the similarity or disparity of these techniques in detecting resting-state brain activity. It is therefore important to assess the static and dynamic characteristics of these fMRI techniques to guide future applications. Here we acquired fMRI data while subjects were in eyes-closed (EC) and eyes-open (EO) states, using both ASL and BOLD techniques, at two research centers (NIDA and HNU). Static brain activity was calculated as voxel-wise mean cerebral blood flow (CBF) using ASL, i.e., CBF-mean, while dynamic activity was measured by the amplitude of low frequency fluctuations (ALFF) of BOLD, i.e., BOLD-ALFF, at both NIDA and HNU, and CBF, i.e., CBF-ALFF, at NIDA. We showed that mean CBF was lower under EC than EO in the primary visual cortex, while BOLD-ALFF was higher under EC in the primary somatosensory cortices extending to the primary auditory cortices and lower in the lateral occipital area. Interestingly, mean CBF and BOLD-ALFF results overlapped at the visual cortex to a very small degree. Importantly, these findings were largely replicated by the HNU dataset. State differences found by CBF-ALFF were located in the primary auditory cortices, which were generally a subset of BOLD-ALFF and showed no spatial overlap with CBF-mean. In conclusion, static brain activity measured by mean CBF and dynamic brain activity measured by BOLD- and CBF-ALFF may reflect different aspects of resting-state brain activity and a combination of ASL and BOLD may provide complementary information on the biophysical and physiological processes of the brain.

Resting-state functional magnetic resonance imaging (RS-fMRI) has been widely used to investigate temporally correlated fluctuations between distributed brain areas, as well as to characterize local synchronization of low frequency (

Although mesial temporal lobe epilepsy (mTLE) is characterized by the pathological changes in mesial temporal lobe, function alteration was also found in extratemporal regions. Our aim is to investigate the information flow between the epileptogenic zone (EZ) and other brain regions. Resting-state functional magnetic resonance imaging (RS-fMRI) data were recorded from 23 patients with left mTLE and matched controls. We first identified the potential EZ using the amplitude of low-frequency fluctuation (ALFF) of RS-fMRI signal, then performed voxel-wise Granger causality analysis between EZ and the whole brain. Relative to controls, patients demonstrated decreased driving effect from EZ to thalamus and basal ganglia, and increased feedback. Additionally, we found an altered causal relation between EZ and cortical networks (default mode network, limbic system, visual network and executive control network). The influence from EZ to right precuneus and brainstem negatively correlated with disease duration, whereas that from the right hippocampus, fusiform cortex, and lentiform nucleus to EZ showed positive correlation. These findings demonstrate widespread brain regions showing abnormal functional interaction with EZ. In addition, increased ALFF in EZ was positively correlated with the increased driving effect on EZ in patients, but not in controls. This finding suggests that the initiation of epileptic activity depends not only on EZ itself, but also on the activity emerging in large-scale macroscopic brain networks. Overall, this study suggests that the causal topological organization is disrupted in mTLE, providing valuable information to understand the pathophysiology of this disorder.

INTRODUCTION: Current pathophysiological theories of schizophrenia highlight the role of altered brain functional and anatomical connectivity. The cognitive division of anterior cingulate cortex (ACC-cd) is a commonly reported abnormal brain region in schizophrenia for its importance in cognitive control process. The aim of this study was to investigate the functional and anatomical connectivity of ACC-cd and its cognitive and clinical manifestation significance in schizophrenia by using the resting-state functional magnetic resonance imaging (fMRI) and the diffusion tensor imaging (DTI). METHODS: Thirty-three medicated schizophrenics and 30 well-matched health controls were recruited. Region-of-interest (ROI)-based resting-state functional connectivity analysis and Tract-Based Spatial Statistics (TBSS) were performed on 30 patients and 30 controls, and 24 patients and 29 controls, respectively. The Pearson correlation was performed between the imaging measures and the Stroop performance and scores of the Positive and Negative Syndrome Scale (PANSS), respectively. RESULTS: Patients with schizophrenia showed significantly abnormal in the functional connectivity and its hemispheric asymmetry of the ACC-cd with multiple brain areas, e.g., decreased positive connectivity with the bilateral putamen and caudate, increased negative connectivity with the left posterior cingulated cortex (PCC), increased asymmetry of connectivity strength with the contralateral inferior frontal gyrus (IFG). The FA of the right anterior cingulum was significantly decreased in patients group (p=0.014). The abnormal functional and structural connectivity of ACC-cd were correlated with Stroop performance and the severity of the symptoms in patients. CONCLUSIONS: Our results suggested that the abnormal connectivity of the ACC-cd might play a role in the cognitive impairment and clinical symptoms in schizophrenia.

Spontaneous brain activity or off-line activity after memory encoding is associated with memory consolidation. A few recent resting-state functional magnetic resonance imaging (RS-fMRI) studies indicate that the RS-fMRI could map off-line memory consolidation effects. However, the gene effects on memory consolidation process remain largely unknown. Here we collected two RS-fMRI sessions, one before and another after an episodic memory encoding task, from two groups of healthy young adults, one with apolipoprotein E (APOE) ε2/ε3 and the other with APOE ε3/ε4. The ratio of regional homogeneity (ReHo), a measure of local synchronization of spontaneous RS-fMRI signal, of the two sessions was used as an index of memory-consolidation. APOE ε3/ε4 group showed greater ReHo ratio within the medial temporal lobe (MTL). The ReHo ratio in MTL was significantly correlated with the recognition memory performance in the APOE ε3/ε4 group but not in ε2/ε3 group. Additionally, APOE ε3/ε4 group showed lower ReHo ratio in the occipital and parietal picture-encoding areas. Our results indicate that APOE ε3/ε4 group may have a different off-line memory consolidation process compared to ε2/ε3 group. These results may help generate future hypotheses that the off-line memory consolidation might be impaired in Alzheimer's disease.

BACKGROUND: Prior research has shown that the amygdala of carriers of the short allele (s) of the serotonin transporter (5-HTT) gene (5-HTTLPR) have a larger response to negative emotional stimuli and higher spontaneous activity during the resting state than non-carriers. However, recent studies have suggested that the effects of 5-HTTLPR may be specific to different ethnic groups. Few studies have been conducted to address this issue. METHODOLOGY/PRINCIPAL FINDINGS: Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) was conducted on thirty-eight healthy Han Chinese subjects (l/l group, n = 19; s/s group, n = 19) during the resting state and during an emotional processing task. Compared with the s/s group, the l/l group showed significantly increased regional homogeneity or local synchronization in the right amygdala during the resting state (|t|>2.028, p

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.