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Some patients after mild traumatic brain injury (mTBI) experience microstructural damages in the long-distance white matter (WM) connections, which disrupts the functional connectome of large-scale brain networks that support cognitive function. Patterns of WM structural damage following mTBI were well documented using diffusion tensor imaging (DTI). However, the functional organization of WM and its association with gray matter functional networks (GM-FNs) and its DTI metrics remain unknown. The present study adopted resting-state functional magnetic resonance imaging to explore WM functional properties in mTBI patients (108 acute patients, 48 chronic patients, 46 healthy controls [HCs]). Eleven large-scale WM functional networks (WM-FNs) were constructed by the k-means clustering algorithm of voxel-wise WM functional connectivity (FC). Compared with HCs, acute mTBI patients observed enhanced FC between inferior fronto-occipital fasciculus (IFOF) WM-FN and primary sensorimotor WM-FNs, and cortical primary sensorimotor GM-FNs. Further, acute mTBI patients showed increased DTI metrics (mean diffusivity, axial diffusivity, and radial diffusivity) in deep WM-FNs and higher-order cognitive WM-FNs. Moreover, mTBI patients demonstrated full recovery of FC and partial recovery of DTI metrics in the chronic stage. Additionally, enhanced FC between IFOF WM-FN and anterior cerebellar GM-FN was correlated with impaired information processing speed. Our findings provide novel evidence for functional and structural alteration of WM-FNs in mTBI patients. Importantly, the convergent damage of the IFOF network might imply its crucial role in our understanding of the pathophysiology mechanism of mTBI patients.

The present study aims to investigate structural and functional connectivity (SC and FC) in cerebello-cerebral circuit in idiopathic generalized epilepsy (IGE). Diffusion tensor imaging and resting-state imaging data were collected from 57 patients with IGE and 66 controls in the present study. First, we performed bidirectional probabilistic fiber tracking between cerebellum and cerebral cortex, consisting of cerebellar efferent and afferent fibers. Then, strength of structural connectivity (SCS), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were extracted and compared between groups. Finally, cerebellar FC with cerebral cortex was evaluated with seeding at dentate nucleus. Between-group comparisons were performed using t tests with a significant level setting at p

BackgroundSchizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks.MethodsWe applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls).ResultsWe demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05).ConclusionsThe compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Impairment of face perception in schizophrenia is a core aspect of social cognitive dysfunction. This impairment is particularly marked in threatening face processing. Identifying reliable neural correlates of the impairment of threatening face processing is crucial for targeting more effective treatments. However, neuroimaging studies have not yet obtained robust conclusions. Through comprehensive literature search, twenty-one whole brain datasets were included in this meta-analysis. Using seed-based d-Mapping, in this voxel-based meta-analysis, we aimed to: 1) establish the most consistent brain dysfunctions related to threating face processing in schizophrenia; 2) address task-type heterogeneity in this impairment; 3) explore the effect of potential demographic or clinical moderator variables on this impairment. Main meta-analysis indicated that patients with chronic schizophrenia demonstrated attenuated activations in limbic emotional system along with compensatory over-activation in medial prefrontal cortex (MPFC) during threatening faces processing. Sub-task analyses revealed under-activations in right amygdala and left fusiform gyrus in both implicit and explicit tasks. The remaining clusters were found to be differently involved in different types of tasks. Moreover, meta-regression analyses showed brain abnormalities in schizophrenia were partly modulated by age, gender, medication and severity of symptoms. Our results highlighted breakdowns in limbic-MPFC circuit in schizophrenia, suggesting general inability to coordinate and contextualize salient threat stimuli. These findings provide potential targets for neurotherapeutic and pharmacological interventions for schizophrenia.

The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

Purpose: The purpose of this study was to comprehensively evaluate alterations of resting-state spontaneous brain activity in patients with idiopathic generalized epilepsy (IGE) and its subgroups [juvenile myoclonic epilepsy (JME) and generalized tonic-clonic seizures (GTCS)]. Methods: Resting state functional magnetic resonance imaging (fMRI) data were acquired from 60 patients with IGE and 60 healthy controls (HCs). Amplitude of low frequency fluctuation (ALFF), global functional connectivity density (gFCD), local FCD (lFCD), and long range FCD (lrFCD) were used to evaluate spontaneous brain activity in the whole brain. Moreover, the coupling between ALFF and FCDs (gFCD, lFCD, and lrFCD) was analyzed on both voxel-wise and subject-wise levels. Two-sample t-tests were used to analyze the difference in ALFF, FCDs and coupling on a subject-wise level between the two groups. Nonparametric permutation tests were used to evaluate differences in coupling on a voxel-wise level. Key findings: Patients with IGE and its subgroups showed reduced ALFF, gFCD and lrFCD in posterior regions of the default mode network (DMN). In addition, decreased ALFF and increased coupling with FCD were found in the cerebellum, while decreased coupling was observed in the bilateral pre- and postcentral gyrus in IGE compared with the coupling in HCs. Similar findings were found in the analysis between each of the two subgroups of IGE (JME and GTCS) and HCs, and JME patients had increased coupling in the cerebellum and bilateral middle occipital gyrus compared with coupling in the GTCS patients. Significance: This study demonstrated a multifactor abnormality of the DMN in IGE and emphasized that the abnormality in the cerebellum was associated with dysfunctional motor symptoms during seizures and might participate in the regulation of GSWDs in IGE.

Juvenile myoclonic epilepsy (JME) is a common type of idiopathic generalized epilepsy that is characterized by myoclonic jerks of the upper limbs and generalized tonic-clonic seizures. Frontal cognitive dysfunctions and abnormal coupling of the thalamocortical system have been found in neuropsychological and neuroimaging studies. This study intended to explore white matter (WM) measurement changes in JME using MRI. Twenty-six patients with JME and 25 healthy controls (HC) were recruited for the acquisition of diffusion MRI and structural MRI data. Then, a tract-based spatial statistics approach was used to investigate the disease effects on WM microstructural diffusion characteristics. Subsequently, the associations between clinical features and characteristics of the tracts that connect the impacted regions were also evaluated. Compared with HC, JME showed an increased mean diffusivity in the anterior corpus callosum connected to the bilateral frontal lobe. Decreased axial diffusivity was observed in the body of the corpus callosum connected to the bilateral supplementary motor area as well as, in the region connecting the left thalamic radiation, the superior longitudinal fasciculus and corticospinal tract. Furthermore, the microstructural metrics of the tracts connecting these regions, especially the projection fibres that connect the cerebral cortex, subcortical regions and cerebellum, were correlated with disease duration. These findings likely reflect the alterations in WM microstructural connectivity, which may be associated with frontal cognitive and motor dysfunction in JME. In addition, the projection fibres connecting these impacted regions are progressively affected by the disease duration. Based on our findings, we propose that the cerebellum may play a potential role in the pathomechanism of JME.