Pretreatment intranetwork connectivity can predict the outcomes in idiopathic tinnitus patients treated with sound therapy

Previous studies demonstrated that brain morphological differences and distinct patterns of neural activation exist in tinnitus patients with different prognoses after sound therapy. This study aimed to explore possible differences in intrinsic network‐level functional connectivity (FC) in patients with different outcomes after sound therapy (narrow band noise). We examined intrinsic FC using resting‐state functional magnetic resonance imaging in 78 idiopathic tinnitus patients (including 35 effectively treated and 43 ineffectively treated) and 52 healthy controls (HCs) via independent component analysis. We also investigated the associations between the differences in FC and clinical variables. Analyses revealed significantly altered intranetwork connectivity in the auditory network (AUN) and some nonauditory‐related networks in the EG/IG patients compared to HCs; compared with EG patients, IG patients showed decreased intranetwork connectivity in the anterior default mode network (aDMN) and AUN. Meanwhile, robust differences were also evident in internetwork connectivity between some nonauditory‐related networks (salience network and executive control network; posterior default mode network and dorsal attention network) in the EG relative to IG patients. We combined intranetwork connectivity in the aDMN and AUN as an imaging indicator to evaluate patient outcomes and screen patients before treatment; this approach reached a sensitivity of 94.3% and a specificity of 76.7%. Our study suggests that tinnitus patients with different outcomes show distinct network‐level functional reorganization patterns. Intranetwork connectivity in the aDMN and AUN may be indicators that can be used to predict prognoses in patients with idiopathic tinnitus and screen patients before sound therapy.     

Outcomes at 6 months are related to brain structural and white matter microstructural reorganization in idiopathic tinnitus patients treated with sound therapy

This study aimed to explore brain structural and white matter microstructural reorganization in the early stage of tinnitus and identify brain alterations that contribute to its relief after 6 months of sound therapy. We studied 64 patients with idiopathic tinnitus, including 29 patients who were categorized into an effective group (EG) and 35 who were categorized into an ineffective group (IG) according to the 6‐month follow‐up improvement of the Tinnitus Handicap Inventory score, along with 63 healthy controls (HCs). All participants underwent structural and diffusion tensor imaging scanning on a 3‐T magnetic resonance system. Differences in brain gray/white matter volume and white matter microstructure were evaluated using voxel‐based morphometry analysis and tract‐based spatial statistics among the three groups. Associations between brain reorganization and the improvement of tinnitus symptoms were also investigated. Compared with EG patients, IG patients experienced a significant gray matter volume decrease in the right middle frontal gyrus (MFG)/right precentral gyrus (PreCG). Meanwhile, both EG and IG patients showed significant changes (decrease or increase) in brain white matter integrity in the auditory‐related or nonauditory‐related white matter fiber tracts compared with HCs, while EG patients showed decreased axial diffusivity in the bilateral middle cerebellar peduncle (MCP) compared with IG patients. We combined the gray matter change of the MFG/PreCG and the white matter integrity of the bilateral MCP as an imaging indicator to evaluate the patient''s prognosis and screen patients before treatment; this approach reached a sensitivity of 77.1% and a specificity of 82.8%. Our study suggests that there was a close relationship between brain reorganization and tinnitus improvement. The right MFG/PreCG and bilateral MCP may be indicators that can be used to predict prognoses in patients with idiopathic tinnitus and may be used to screen patients before sound therapy. These findings may provide new useful information that can lead to a better understanding of the tinnitus mechanism.     

Aberrant dynamic structure–function relationship of rich‐club organization in treatment‐naïve newly diagnosed juvenile myoclonic epilepsy

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.     

Disrupted topological organization of resting‐state functional brain networks in cerebral small vessel disease

We aimed to investigate alterations in functional brain networks and assess the relationship between functional impairment and topological network changes in cerebral small vessel disease (CSVD) patients with and without cerebral microbleeds (CMBs). We constructed individual whole‐brain, region of interest (ROI) level functional connectivity (FC) networks for 24 CSVD patients with CMBs (CSVD‐c), 42 CSVD patients without CMBs (CSVD‐n), and 36 healthy controls (HCs). Then, we used graph theory analysis to investigate the global and nodal topological disruptions between groups and relate network topological alterations to clinical parameters. We found that both the CSVD and control groups showed efficient small‐world organization in FC networks. However, compared to CSVD‐n patients and controls, CSVD‐c patients exhibited a significantly decreased clustering coefficient, global efficiency, and local efficiency and an increased shortest path length, indicating a disrupted balance between local specialization and global integration in FC networks. Although both the CSVD and control groups showed highly similar hub distributions, the CSVD‐c group exhibited significantly altered nodal betweenness centrality (BC), mainly distributed in the default mode network (DMN), attention, and visual functional areas. There were almost no global or regional alterations between CSVD‐n patients and controls. Furthermore, the altered nodal BC of the right anterior/posterior cingulate gyrus and left cuneus were significantly correlated with cognitive parameters in CSVD patients. These results suggest that CSVD patients with and without CMBs had segregated disruptions in the topological organization of the intrinsic functional brain network. This study advances our current understanding of the pathophysiological mechanisms underlying CSVD.     

Aberrant functional brain network dynamics in patients with functional constipation

The aberrant static functional connectivity of brain network has been widely investigated in patients with functional constipation (FCon). However, the dynamics of brain functional connectivity in FCon patients remained unknown. This study aimed to detect the brain dynamics of functional connectivity states and network topological organizations of FCon patients and investigate the correlations of the aberrant brain dynamics with symptom severity. Eighty‐three FCon patients and 80 healthy subjects (HS) were included in data analysis. The spatial group independent component analysis, sliding‐window approach, k‐means clustering, and graph‐theoretic analysis were applied to investigate the dynamic temporal properties and coupling patterns of functional connectivity states, as well as the time‐variation of network topological organizations in FCon patients. Four reoccurring functional connectivity states were identified in k‐means clustering analysis. Compared to HS, FCon patients manifested the lower occurrence rate and mean dwell time in the state with a complex connection between default mode network and cognitive control network, as well as the aberrant anterior insula–cortical coupling patterns in this state, which were significantly correlated with the symptom severity. The graph‐theoretic analysis demonstrated that FCon patients had higher sample entropy at the nodal efficiency of anterior insula than HS. The current findings provided dynamic perspectives for understanding the brain connectome of FCon and laid the foundation for the potential treatment of FCon based on brain connectomics.     

Altered brain functional network dynamics in obsessive–compulsive disorder

Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.     

Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities

The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data‐driven parcellation and automated‐linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion‐weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data‐driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity‐based multimodal data fusion in brain.     

Surface‐Based Connectivity Integration: An atlas‐free approach to jointly study functional and structural connectivity

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra‐cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC‐FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high‐quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas‐based methods, this atlas‐free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.     

Modulation effect of acupuncture treatment on chronic neck and shoulder pain in female patients: Evidence from periaqueductal gray‐based functional connectivity

AimsChronic neck and shoulder pain (CNSP) is a common neurological disorder, which females are more likely to suffer from. The periaqueductal gray (PAG) plays a key role in the descending modulation of pain. This study aimed to investigate altered PAG‐based functional connectivity (FC) in female patients with CNSP related to healthy controls (HCs) and the effect of acupuncture for female patients with CNSP using PAG‐based FC biomarkers.MethodsPAG‐based FC value was calculated based on resting‐state functional images and then compared between patients with CNSP at pre‐acupuncture, post‐acupuncture, and HCs. Then, correlational analyses were performed to examine the relationships between increased PAG‐based FC strength and improved clinical parameters in patients after acupuncture treatment.ResultsBefore acupuncture treatment, compared to HCs, patients with CSNP showed altered PAG‐based FC with widely distributed brain regions, including the left medial superior frontal gyrus, bilateral posterior insula (pIns), and cingulate gyrus. After treatment, patients with CNSP exhibited specially improved PAG‐pIns FC compared to that before treatment, and no significant difference was observed in the increased PAG‐pIns FC strength between HCs and patients with CNSP after treatment. Furthermore, pain catastrophizing reduction was significantly correlated with the increased PAG‐pIns FC strength in patients after treatment.ConclusionThe effect of acupuncture treatment may relate to the increased PAG‐pIns FC, which significantly correlated with pain catastrophizing reduction after treatment. These findings shed important mechanistic information on the role of therapeutic approaches in treating chronic neck and shoulder pain.     

Interrelating differences in structural and functional connectivity in the older adult's brain

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age‐related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure–function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting‐state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55–85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting‐state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age‐related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age‐related decreases. Because this connectivity profile was associated with the most severe age‐related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.     

Identification of essential tremor based on resting‐state functional connectivity

Currently, machine‐learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole‐brain resting‐state functional connectivity (RSFC) metrics combined with machine‐learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET‐related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine‐learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello‐thalamo‐motor and non‐motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine‐learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.     

Aberrant structural–functional coupling in adult cannabis users

Cellular studies indicate that endocannabinoid type‐1 retrograde signaling plays a major role in synaptic plasticity. Disruption of these processes by delta‐9‐tetrahydrocannabinol (THC) could produce alterations either in structural and functional brain connectivity or in their association in cannabis (CB) users. Graph theoretic structural and functional networks were generated with diffusion tensor imaging and resting‐state functional imaging in 37 current CB users and 31 healthy non‐users. The primary outcome measures were coupling between structural and functional connectivity, global network characteristics, association between the coupling and network properties, and measures of rich‐club organization. Structural–functional (SC–FC) coupling was globally preserved showing a positive association in current CB users. However, the users had disrupted associations between SC–FC coupling and network topological characteristics, most perturbed for shorter connections implying region‐specific disruption by CB use. Rich‐club analysis revealed impaired SC–FC coupling in the hippocampus and caudate of users. This study provides evidence of the abnormal SC–FC association in CB users. The effect was predominant in shorter connections of the brain network, suggesting that the impact of CB use or predispositional factors may be most apparent in local interconnections. Notably, the hippocampus and caudate specifically showed aberrant structural and functional coupling. These structures have high CB1 receptor density and may also be associated with changes in learning and habit formation that occur with chronic cannabis use.     

Risk‐taking in the human brain: An activation likelihood estimation meta‐analysis of the balloon analog risk task (BART)

The Balloon Analog Risk Task (BART) is increasingly used to assess risk‐taking behavior and brain function. However, the brain networks underlying risk‐taking during the BART and its reliability remain controversial. Here, we combined the activation likelihood estimation (ALE) meta‐analysis with both task‐based and task‐free functional connectivity (FC) analysis to quantitatively synthesize brain networks involved in risk‐taking during the BART, and compared the differences between adults and adolescents studies. Based on 22 pooled publications, the ALE meta‐analysis revealed multiple brain regions in the reward network, salience network, and executive control network underlying risk‐taking during the BART. Compared with adult risk‐taking, adolescent risk‐taking showed greater activation in the insula, putamen, and prefrontal regions. The combination of meta‐analytic connectivity modeling with task‐free FC analysis further confirmed the involvement of the reward, salience, and cognitive control networks in the BART. These findings demonstrate the core brain networks for risk‐taking during the BART and support the utility of the BART for future neuroimaging and developmental research.     

Validating dynamicity in resting state fMRI with activation‐informed temporal segmentation

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test–retest reliability. We hypothesize that time‐varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data‐driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole‐brain functional activation, rather than a fixed‐length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block‐design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject‐specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole‐brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time‐varying FC in rest.     

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high‐order functional connectivity

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM‐related dementia is still less understood. Recent resting‐state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM‐CI). However, they mainly used a mass‐univariate statistical analysis that was not suitable to reveal the altered FC “pattern” in T2DM‐CI, due to lower sensitivity. In this study, we proposed to use high‐order FC to reveal the abnormal connectomics pattern in T2DM‐CI with a multivariate, machine learning‐based strategy. We also investigated whether such patterns were different between T2DM‐CI and T2DM without cognitive impairment (T2DM‐noCI) to better understand T2DM‐induced cognitive impairment, on 23 T2DM‐CI and 27 T2DM‐noCI patients, as well as 50 healthy controls (HCs). We first built the large‐scale high‐order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM‐CI (as well as T2DM‐noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM‐CI versus HC differentiation, but only 59.62% in T2DM‐noCI versus HC classification. We found abnormal high‐order FC patterns in T2DM‐CI compared to HC, which was different from that in T2DM‐noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM‐induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.     

Altered resting‐state functional network connectivity in profound sensorineural hearing loss infants within an early sensitive period: A group ICA study

Data from both animal models and deaf children provide evidence for that the maturation of auditory cortex has a sensitive period during the first 2–4 years of life. During this period, the auditory stimulation can affect the development of cortical function to the greatest extent. Thus far, little is known about the brain development trajectory after early auditory deprivation within this period. In this study, independent component analysis (ICA) technique was used to detect the characteristics of brain network development in children with bilateral profound sensorineural hearing loss (SNHL) before 3 years old. Seven resting‐state networks (RSN) were identified in 50 SNHL and 36 healthy controls using ICA method, and further their intra‐and inter‐network functional connectivity (FC) were compared between two groups. Compared with the control group, SNHL group showed decreased FC within default mode network, while enhanced FC within auditory network (AUN) and salience network. No significant changes in FC were found in the visual network (VN) and sensorimotor network (SMN). Furthermore, the inter‐network FC between SMN and AUN, frontal network and AUN, SMN and VN, frontal network and VN were significantly increased in SNHL group. The results implicate that the loss and the compensatory reorganization of brain network FC coexist in SNHL infants. It provides a network basis for understanding the brain development trajectory after hearing loss within early sensitive period.     

Increased functional interaction within frontoparietal network during working memory task in major depressive disorder

Abnormal fronto‐parietal activation has been suggested as a neural underpinning of the working memory (WM) deficits in major depressive disorder (MDD). However, the potential interaction within the frontoparietal network during WM processing in MDD remains unclear. This study aimed to examine the role of abnormal functional interactions within frontoparietal network in the neuropathological mechanisms of WM deficits in MDD. A total of 40 MDD patients and 47 demographic matched healthy controls (HCs) were included. Functional magnetic resonance imaging and behavioral data were collected during numeric n‐back tasks. The psychophysiological interaction and dynamic causal modelling methods were applied to investigate the connectivity within the frontoparietal network in MDD during n‐back tasks. The psychophysiological interaction analysis revealed that MDD patients showed increased functional connectivity between the right inferior parietal lobule (IPL) and the right dorsolateral prefrontal cortex (dlPFC) compared with HCs during the 2‐back task. The dynamic causal modelling analysis revealed that MDD patients had significantly increased forward modulation connectivity from the right IPL to the right dlPFC than HCs during the 2‐back task. Partial correlation was used to calculate the relationship between connective parameters and psychological variables in the MDD group, which showed that the effective connectivity from right IPL to right dlPFC was correlated negatively with the sensitivity index d’ of WM performances and positively with the depressive severity in MDD group. In conclusion, the abnormal functional and effective connectivity between frontal and parietal regions might contribute to explain the neuropathological mechanism of working memory deficits in major depressive disorder.     

Novel noninvasive identification of patient‐specific epileptic networks in focal epilepsies: Linking single‐photon emission computed tomography perfusion during seizures with resting‐state magnetoencephalography dynamics

Single‐photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug‐resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase‐locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper‐, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network.     

Resting‐state functional connectivity of the human hippocampus in periadolescent children: Associations with age and memory performance

The hippocampus is necessary for declarative (relational) memory, and the ability to form hippocampal‐dependent memories develops through late adolescence. This developmental trajectory of hippocampal‐dependent memory could reflect maturation of intrinsic functional brain networks, but resting‐state functional connectivity (rs‐FC) of the human hippocampus is not well‐characterized for periadolescent children. Measuring hippocampal rs‐FC in periadolescence would thus fill a gap, and testing covariance of hippocampal rs‐FC with age and memory could inform theories of cognitive development. Here, we studied hippocampal rs‐FC in a cross‐sectional sample of healthy children (N = 96; 59 F; age 9–15 years) using a seed‐based approach, and linked these data with NIH Toolbox measures, the Picture‐Sequence Memory Test (PSMT) and the List Sorting Working Memory Test (LSWMT). The PSMT was expected to rely more on hippocampal‐dependent memory than the LSWMT. We observed hippocampal rs‐FC with an extensive brain network including temporal, parietal, and frontal regions. This pattern was consistent with prior work measuring hippocampal rs‐FC in younger and older samples. We also observed novel, regionally specific variation in hippocampal rs‐FC with age and hippocampal‐dependent memory but not working memory. Evidence consistent with these findings was observed in a second, validation dataset of similar‐age healthy children drawn from the Philadelphia Neurodevelopment Cohort. Further, a cross‐dataset analysis suggested generalizable properties of hippocampal rs‐FC and covariance with age and memory. Our findings connect prior work by describing hippocampal rs‐FC and covariance with age and memory in typically developing periadolescent children, and our observations suggest a developmental trajectory for brain networks that support hippocampal‐dependent memory.     

Segregation,integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested‐spectral partition (NSP) method to study the segregation, integration, and balance in resting‐state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine‐learning approach, we identified different system‐level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention‐deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.