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Even with an overarching functional dysconnectivity model of adolescent‐onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group‐level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual‐specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual‐level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension‐specific psychotic symptoms in 30 antipsychotic‐naïve first‐episode AOS patients (recruited sample of 39). Age‐related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic‐naïve first‐episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual‐specific FC biomarkers could significantly and better predict AOS positive‐dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN–SN connection exhibited aberrant age‐associated alteration in AOS. The individual‐level findings reveal reproducible FPN‐based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent‐onset disorders., Reliable frontoparietal control network‐based functional connectome biomarkers underlying positive symptom domains could be identified at the individual level, but not at the group level. Replication results indicated relatively better generalization ability of the prediction model by using individual‐based strategy compared with group‐level approach.

Epilepsy is a common brain network disorder associated with disrupted large‐scale excitatory and inhibitory neural interactions. Recent resting‐state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical–cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy., This study demonstrated spatial shifts of non‐uniform GS topography across higher‐order prefrontal cortex and sensory areas in generalized and focal epilepsy. We found that these abnormal GS topography patterns were likely to evolve over the course of a longer disease. Our findings have potential implications for understanding the neuronal mechanisms of disrupted global excitation/inhibition balance in epilepsy.

A major challenge in neuroscience is understanding how brain function emerges from the connectome. Most current methods have focused on quantifying functional connectomes in gray‐matter (GM) signals obtained from functional magnetic resonance imaging (fMRI), while signals from white‐matter (WM) have generally been excluded as noise. In this study, we derived a functional connectome from WM resting‐state blood‐oxygen‐level‐dependent (BOLD)‐fMRI signals from a large cohort (n = 488). The WM functional connectome exhibited weak small‐world topology and nonrandom modularity. We also found a long‐term (i.e., over 10 months) topological reliability, with topological reproducibility within different brain parcellation strategies, spatial distance effect, global and cerebrospinal fluid signals regression or not. Furthermore, the small‐worldness was positively correlated with individuals' intelligence values (r = .17, p (corrected) = .0009). The current findings offer initial evidence using WM connectome and present additional measures by which to uncover WM functional information in both healthy individuals and in cases of clinical disease.

Neural circuit dysfunction underlies the biological mechanisms of suicidal ideation (SI). However, little is known about how the brain's “dynome” differentiate between depressed patients with and without SI. This study included depressed patients (n = 48) with SI, without SI (NSI), and healthy controls (HC, n = 30). All participants underwent resting‐state functional magnetic resonance imaging. We constructed dynamic and static connectomics on 200 nodes using a sliding window and full‐length time–series correlations, respectively. Specifically, the temporal variability of dynamic connectomic was quantified using the variance of topological properties across sliding window. The overall topological properties of both static and dynamic connectomics further differentiated between SI and NSI, and also predicted the severity of SI. The SI showed decreased overall topological properties of static connectomic relative to the HC. The SI exhibited increases in overall topological properties with regard to the dynamic connectomic when compared with the HC and the NSI. Importantly, combining the overall topological properties of dynamic and static connectomics yielded mean 75% accuracy (all p

Traditional task‐evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low‐frequency steady‐state brain response (lfSSBR) reflects frequency‐tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra‐slow frequency band ( .955) of their spatial distribution and brain–behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency‐specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks.

In mesial temporal lobe epilepsy (mTLE), the causal relationship of morphometric alterations between hippocampus and the other regions, that is, how the hippocampal atrophy leads to progressive morphometric alterations in the epileptic network regions remains largely unclear. In this study, a causal network of structural covariance (CaSCN) was proposed to map the causal effects of hippocampal atrophy on the network-based morphometric alterations in mTLE. It was hypothesized that if cross-sectional morphometric MRI data could be attributed temporal information, for example, by sequencing the data according to disease progression information, GCA would be a feasible approach for constructing a CaSCN. Based on a large cohort of mTLE patients (n = 108), the hippocampus-associated CaSCN revealed that the hippocampus and the thalamus were prominent nodes exerting causal effects (i.e., GM reduction) on other regions and that the prefrontal cortex and cerebellum were prominent nodes being subject to causal effects. Intriguingly, compensatory increased gray matter volume in the contralateral temporal region and post cingulate cortex were also detected. The method unraveled richer information for mapping network atrophy in mTLE relative to the traditional methods of stage-specific comparisons and structured covariance network. This study provided new evidence on the network spread mechanism in terms of the causal influence of hippocampal atrophy on progressive brain structural alterations in mTLE. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Network studies have demonstrated that a small set of highly connected regions may play a central role in global information integration, together forming a rich club organization. Given that generalized tonic-clonic seizure (GTCS) has been conceptualized as a network disorder, we hypothesized that the rich club disturbances may be related to the network abnormalities of GTCS. Here, we used graph theoretical analysis to investigate the rich club organization of both structural and functional connectome in patients with GTCS (n = 50) and healthy controls (n = 60). We further measured the level of global efficiency and clustering in rich club and non-rich club organization. We, respectively, identified a small number of highly connected hubs as rich club organization from structural and functional networks. Patients were found to exhibit significantly reduced rich club connectivity among the central hubs. Meanwhile, both structural and functional network showed changed levels of global efficiency and clustering of rich club organization in GTCS. Furthermore, in patients, lower levels of rich club connectivity were found to be correlated with longer duration of illness and seizure frequency. Together, these findings suggest that GTCS is characterized by a selective disruption of rich club organization due to the long-term injurious effects of epileptic actions on the central hub regions, which potentially contribute to a reduced level of brain integration capacity among different functional domains and an added effect of illness on a preexisting vulnerability. Our findings emphasize a central role for abnormal rich club organization in the pathophysiological mechanism underlying GTCS. Hum Brain Mapp 37:4487–4499, 2016. © 2016 Wiley Periodicals, Inc.

Time-invariant resting-state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting-state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k-means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta-analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio-cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting-state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD., Fundamental Research Funds for the Central Universities. Grant Numbers: 2672018ZYGX2018J079, ZYGX2016J187 National Institute for Health Research Cambridge Biomedical Resource Centre National Natural Science Foundation of China. Grant Numbers: 61533006, 61673089, 81771919, 81871432 Sichuan Science and Technology Program. Grant Number: 2018TJPT0016 Specialized Research Fund for the Doctoral Program of Higher Education of China. Grant Number: 20120185110028

Benign epilepsy with centrotemporal spikes (BECTS) is often associated with neural circuit dysfunction, particularly during the transient active state characterized by interictal epileptiform discharges (IEDs). Little is known, however, about the functional neural circuit abnormalities in BECTS without IEDs, or if such abnormalities could be used to differentiate BECTS patients without IEDs from healthy controls (HCs) for early diagnosis. To this end, we conducted resting-state functional magnetic resonance imaging (RS-fMRI) and simultaneous Electroencephalogram (EEG) in children with BECTS (n = 43) and age-matched HC (n = 28). The simultaneous EEG recordings distinguished BECTS with IEDs (n = 20) from without IEDs (n = 23). Intrinsic brain activity was measured in all three groups using the amplitude of low frequency fluctuation at rest. Compared to HC, BECTS patients with IEDs exhibited an intrinsic activity abnormality in the thalamus, suggesting that thalamic dysfunction could contribute to IED emergence while patients without IEDs exhibited intrinsic activity abnormalities in middle frontal gyrus and superior parietal gyrus. Using multivariate pattern classification analysis, we were able to differentiate BECTS without IEDs from HCs with 88.23% accuracy. BECTS without epileptic transients can be distinguished from HC and BECTS with IEDs by unique regional abnormalities in resting brain activity. Both transient abnormalities as reflected by IEDs and chronic abnormalities as reflected by RS-fMRI may contribute to BECTS development and expression. Intrinsic brain activity and multivariate pattern classification techniques are promising tools to diagnose and differentiate BECTS syndromes. Hum Brain Mapp 36:3878-3889, 2015. © 2015 Wiley Periodicals, Inc.

Benign epilepsy with centrotemporal spikes (BECTS) is characterized by abnormal (static) functional interactions among cortical and subcortical regions, regardless of the active or chronic epileptic state. However, human brain connectivity is dynamic and associated with ongoing rhythmic activity. The dynamic functional connectivity (dFC) of the distinct striato-cortical circuitry associated with or without interictal epileptiform discharges (IEDs) are poorly understood in BECTS. Herein, we captured the pattern of dFC using sliding window correlation of putamen subregions in the BECTS (without IEDs, n = 23; with IEDs, n = 20) and sex- and age-matched healthy controls (HCs, n = 28) during rest. Furthermore, we quantified dFC variability using their standard deviation. Compared with HCs and patients without IEDs, patients with IEDs exhibited excessive variability in the dorsal striatal-sensorimotor circuitry related to typical seizure semiology. By contrast, excessive stability (decreased dFC variability) was found in the ventral striatal-cognitive circuitry (p

r Abstract: The basal ganglia, a brain structure related to motor control, is implicated in the modulation of epileptic discharges generalization in patients with idiopathic generalized epilepsy (IGE). Using group independent component analysis (ICA) on resting-state fMRI data, this study identified a resting state functional network that predominantly consisted of the basal ganglia in both healthy controls and patients with IGE. In order to gain a better understanding of the basal ganglia network(BGN) in IGE patients, we compared the BGN functional connectivity of controls with that of epilepsy patients, ei- ther with interictal epileptic discharges (with-discharge period, WDP) or without epileptic discharge (nondischarge period, NDP) while scanning. Compared with controls, functional connectivity of BGN in IGE patients demonstrated significantly more integration within BGN except cerebellum and supple- mentary motor area (SMA) during both periods. Compared with the NDP group, the increased func- tional connectivity was found in bilateral caudate nucleus and the putamen, and decreases were observed in the bilateral cerebellum and SMA in WDP group. In accord with the proposal that the ba- sal ganglia modulates epileptic discharge activity, the results showed that the modulation enhanced the integration in BGN of patients, and modulation during WDP was stronger than that during NDP. Furthermore, reduction of functional connectivity in cerebellum and SMA, the abnormality might be further aggravated during WDP, was consistent with the behavioral manifestations with disturbed motor function in IGE. These resting-state fMRI findings in the current study provided evidence confirming the role of the BGN as an important modulator in IGE. Hum Brain Mapp 33:1279-1294, 2012. V C 2011 Wiley Periodicals, Inc.

Various functional imaging tools have been used to detect epileptic activity in the neural network underlying mesial temporal lobe epilepsy (mTLE). In the present fMRI study, a data‐driven approach was employed to map interictal epileptic activity in mTLE patients by measuring the amplitude of low‐frequency fluctuation (ALFF) of the blood oxygen level‐dependent (BOLD) signal. Twenty‐four left mTLE patients and 26 right mTLE patients were investigated by comparing with 25 healthy subjects. In the patients, the regions showing increased ALFF were consistently distributed in the mesial temporal lobe, thalamus, and a few of other cortical and subcortical structures composing a mesial temporal epilepsy network proposed previously, while the regions showing decreased ALFF were mostly located in the areas of so‐called default‐mode network. Data of simultaneous EEG‐fMRI from a portion of the patients suggested that the increases in ALFF might be associated with the interictal epileptic activity. Individual analyses based on statistic parametric mapping revealed a moderate sensitivity and a fairly high specificity for the lateralization of unilateral mTLE. We conclude that the ALFF analysis may provide a useful tool in fMRI study of epilepsy. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.

Benign epilepsy with centrotemporal spikes (BECTS) is often associated with neural circuit dysfunction, particularly during the transient active state characterized by interictal epileptiform discharges (IEDs). Little is known, however, about the functional neural circuit abnormalities in BECTS without IEDs, or if such abnormalities could be used to differentiate BECTS patients without IEDs from healthy controls (HCs) for early diagnosis. To this end, we conducted resting‐state functional magnetic resonance imaging (RS‐fMRI) and simultaneous Electroencephalogram (EEG) in children with BECTS (n = 43) and age‐matched HC (n = 28). The simultaneous EEG recordings distinguished BECTS with IEDs (n = 20) from without IEDs (n = 23). Intrinsic brain activity was measured in all three groups using the amplitude of low frequency fluctuation at rest. Compared to HC, BECTS patients with IEDs exhibited an intrinsic activity abnormality in the thalamus, suggesting that thalamic dysfunction could contribute to IED emergence while patients without IEDs exhibited intrinsic activity abnormalities in middle frontal gyrus and superior parietal gyrus. Using multivariate pattern classification analysis, we were able to differentiate BECTS without IEDs from HCs with 88.23% accuracy. BECTS without epileptic transients can be distinguished from HC and BECTS with IEDs by unique regional abnormalities in resting brain activity. Both transient abnormalities as reflected by IEDs and chronic abnormalities as reflected by RS‐fMRI may contribute to BECTS development and expression. Intrinsic brain activity and multivariate pattern classification techniques are promising tools to diagnose and differentiate BECTS syndromes. Hum Brain Mapp 36:3878–3889, 2015. © 2015 Wiley Periodicals, Inc.

Amplitude and functional connectivity are two fundamental parameters for describing the spontaneous brain fluctuations. These two parameters present close coupling in physiological state, and present different alteration patterns in epilepsy revealed by functional MRI (fMRI). We hypothesized that the alteration of coupling between these two imaging parameters may be underpinned by specific pathological factors of epilepsy, and can be employed to improve the capability for epileptic focus detection. Forty‐seven patients (26 left‐ and 21 right‐sided) with mesial temporal lobe epilepsy (mTLE) and 32 healthy controls underwent resting‐state fMRI scans. All patients were detected to have interictal epileptic discharges on simultaneous electroencephalograph (EEG) recordings. Amplitude‐connectivity coupling was calculated by correlating amplitude and functional connectivity density of low‐frequency brain fluctuations. We observed reduced amplitude‐connectivity coupling associated with epileptic discharges in the mesial temporal regions in both groups of patients, and increased coupling associated with epilepsy durations in the posterior regions of the default‐mode network in the right‐sided patients. Moreover, we proposed a new index of amplitude subtracting connectivity, which elevated imaging contrast for differentiating the patients from the controls. The findings indicated that epileptic discharges and chronic damaging effect of epilepsy might both contribute to alterations of amplitude‐connectivity coupling in different pivotal regions in mTLE. Investigation on imaging coupling provides synergistic approach for describing brain functional changing features in epilepsy. Hum Brain Mapp 36:2756–2766, 2015. © 2015 Wiley Periodicals, Inc.