Ventral Default Mode Network Research Articles

Alteration of spatial patterns at the network-level in facial synkinesis: an independent component and connectome analysis.

BackgroundThe treatment of post-facial palsy synkinesis (PFPS) remains inadequate. Previous studies have confirmed that brain plasticity is involved in the process of functional restoration. Isolated activation has been well studied, however, the brain works as an integrity of several isolated regions. This study aimed to assess the alteration of the brain network topology with overall and local characteristics of information dissemination. Understanding the neural mechanisms of PFPS could help to improve therapy options and prognosis.MethodsPatients with facial synkinesis and healthy controls (HCs) were estimated using functional magnetic resonance imaging (fMRI) of resting-state. Subsequently, an independent component analysis (ICA) was used to extract four subnets from the whole brain. Then we used the measurements of graph theory and calculated in the whole-brain network and each sub-network.ResultsWe found no significant difference between the patient group and the HCs on the whole-brain scale. Then we identified four subnetworks from the resting-state data. In the sub-network property analysis, patients’ locally distributed properties in the sensorimotor network (SMN) and ventral default mode network (vDMN) were reduced. It revealed that γ (10,000 permutations, P=0.048) and S (10,000 permutations, P=0.022) within the SMN progressively decreased in patients with PFPS. For the analysis of vDMN, significant differences were found in γ (10,000 permutations, P=0.019), Elocal (10,000 permutations, P=0.008), and β (10,000 permutations, P=0.011) between the groups.ConclusionsOur results demonstrated a reduction in local network processing efficiency in patients with PFPS. Therefore, we speculate that decreased characteristics in the intra-vDMN and intra-SMN, rather than the whole-brain network, may serve distinct symptoms such as facial nerve damage or more synkinetic movements. This finding of the alteration of network properties is a small step forward to help uncover the underlying mechanism.

Default mode and salience network alterations in suicidal and non-suicidal self-injurious thoughts and behaviors in adolescents with depression

Suicidal ideation (SI) and non-suicidal self-injury (NSSI) are two distinct yet often co-occurring risk factors for suicide deaths in adolescents. Elucidating the neurobiological patterns that specifically characterize SI and NSSI in adolescents is needed to inform the use of these markers in intervention studies and to develop brain-based treatment targets. Here, we clinically assessed 70 adolescents—49 adolescents with depression and 21 healthy controls—to determine SI and NSSI history. Twenty-eight of the depressed adolescents had a history of SI and 29 had a history of NSSI (20 overlapping). All participants underwent a resting-state fMRI scan. We compared groups in network coherence of subdivisions of the central executive network (CEN), default mode network (DMN), and salience network (SN). We also examined group differences in between-network connectivity and explored brain-behavior correlations. Depressed adolescents with SI and with NSSI had lower coherence in the ventral DMN compared to those without SI or NSSI, respectively, and healthy controls (all ps < 0.043, uncorrected). Depressed adolescents with NSSI had lower coherence in the anterior DMN and in insula-SN (all ps < 0.030, uncorrected), and higher CEN–DMN connectivity compared to those without NSSI and healthy controls (all ps < 0.030, uncorrected). Lower network coherence in all DMN subnetworks and insula-SN were associated with higher past-month SI and NSSI (all ps < 0.001, uncorrected). Thus, in our sample, both SI and NSSI are related to brain networks associated with difficulties in self-referential processing and future planning, while NSSI specifically is related to brain networks associated with disruptions in interoceptive awareness.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks.

SummaryFunctional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Dual n-back training improves functional connectivity of the right inferior frontal gyrus at rest

Several studies have shown that the benefits of working memory (WM) training can be attributed to functional and structural neural changes in the underlying neural substrate. In the current study, we investigated whether the functional connectivity of the brain at rest in the default mode network (DMN) changes with WM training. We varied the complexity of the training intervention so, that half of the participants attended dual n-back training whereas the other half attended single n-back training. This way we could assess the effects of different training task parameters on possible connectivity changes. After 16 training sessions, the dual n-back training group showed improved performance accompanied by increased functional connectivity of the ventral DMN in the right inferior frontal gyrus, which correlated with improvements in WM. We also observed decreased functional connectivity in the left superior parietal cortex in this group. The single n-back training group did not show significant training-related changes. These results show that a demanding short-term WM training intervention can alter the default state of the brain.

Resting-state default mode network connectivity in young individuals with Down syndrome.

BackgroundDown syndrome (DS) is a chromosomal disorder that causes intellectual disability. Few studies have been conducted on functional connectivity using resting‐state fMRI (functional magnetic resonance imaging) signals or more specifically, on the relevant structure and density of the default mode network (DMN). Although data on this issue have been reported in adult DS individuals (age: >45 years), the DMN properties in young DS individuals have not been studied. The aim of this study was to describe the density and structure of the DMN network from fMRI signals in young DS (age: <36 years).MethodA sample of 22 young people with DS between the ages of 16 and 35 (M = 25.5 and SD = 5.1) was recruited in various centers for people with intellectual disability (ID). In addition to sociodemographic data, a six‐minute fMRI session was recorded with a 3. T Philips Ingenia scanner. A control group of 22 young people, matched by age and gender, was obtained from the Human Connectome Project (to compare the networks properties between groups).ResultsThe values of the 48 ROIs that configured the DMN were obtained, and the connectivity graphs for each subject, the average connectivity graph for each group, the clustering and degree values for each ROI, and the average functional connectivity network were estimated.ConclusionsA higher density of overactivation was identified in DS group in the ventral, sensorimotor, and visual DMN networks, although within a framework of a wide variability of connectivity patterns in comparison with the control group network. These results extend our understanding of the functional connectivity networks pattern and intrasubject variability in DS.

Psychological resilience is correlated with dynamic changes in functional connectivity within the default mode network during a cognitive task

Resilience is a dynamic process that enables organisms to cope with demanding environments. Resting-state functional MRI (fMRI) studies have demonstrated a negative correlation between resilience and functional connectivities (FCs) within the default mode network (DMN). Considering the on-demand recruitment process of resilience, dynamic changes in FCs during cognitive load increases may reflect essential aspects of resilience. We compared DMN FC changes in resting and task states and their association with resilience. Eighty-nine healthy volunteers completed the Connor–Davidson Resilience Scale (CD-RISC) and an fMRI with an auditory oddball task. The fMRI time series was divided into resting and task periods. We focused on FC changes between the latter half of the resting period and the former half of the task phase (switching), and between the former and latter half of the task phase (sustaining). FCs within the ventral DMN significantly increased during “switching” and decreased during “sustaining”. For FCs between the retrosplenial/posterior cingulate and the parahippocampal cortex, increased FC during switching was negatively correlated with CD-RISC scores. In individuals with higher resilience, ventral DMN connectivities were more stable and homeostatic in the face of cognitive demand. The dynamic profile of DMN FCs may represent a novel biomarker of resilience.

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.

Alterations in sympathetic and parasympathetic brain networks in obstructive sleep apnea

Objective/backgroundObstructive sleep apnea (OSA) patients experience hypoxia and, potentially, autonomic impairments stemming from neural damage. In this study, the executive control networks (ECNs), salience networks (SNs), and default mode networks (DMNs) of adult OSA patients, as well as their relationships with autonomic impairment, were investigated through independent component analysis (ICA). Patients/methodsA total of 41 OSA patients and 19 healthy controls volunteers were recruited and subjected to polysomnography to ascertain their degree, if any, of sleep apnea. Each participant also underwent a cardiovascular autonomic survey, with the participant's baroreflex sensitivity (BRS) being determined based on heart rate and blood pressure alterations. The resting fMRI data of the participants was separated using probabilistic ICA, and six autonomic resting-state networks were established for group comparisons. The differences in autonomic parameters, autonomic functional connectivity (FC), and clinical severity were then correlated. ResultsThe OSA group had significantly worse BRS values than the controls, as well as lower FC in the posterior and anterior SNs, bilateral ECNs, and the ventral DMN, and higher FC in the left ECN. These intrinsic connectivity networks showed dissociable correlations with greater baroreflex impairment and clinical disease severity. The higher FC in the left ECN was associated with the lower FC in the ventral DMN. ConclusionsOur findings suggest that autonomic dysfunction in OSA might be accompanied by central autonomic network alterations. The stronger sympathetic-associated regions in ECNs and the weaker parasympathetic-associated regions in DMNs may represent intrinsic neural architecture fluctuations underlining their consequent processes in OSA.

S161. DYNAMIC FUNCTIONAL NETWORK CONNECTIVITY COMPARING AUDITORY VERBAL HALLUCINATIONS IN PSYCHOTIC AND NON-PSYCHOTIC SUBJECTS

BackgroundAuditory verbal hallucinations (AVH) are often seen as a hallmark of schizophrenia, but can also occur in the general healthy population. While AVH in non-clinical populations might offer an opportunity to study them in isolation, it remains debatable whether the mechanisms underlying AVH are the same in clinical and non-clinical populations. For example, non-clinical populations are reported to attribute lower emotional valence to their AVH. Such differences in phenomenology are hypothesized to arise from differences on the neurobiological level. With the current study, we employ a data-driven approach to define brain networks involved in AVH in clinical and non-clinical subjects, and test whether dynamic differences in network connectivity exist between these groups.MethodsFunctional magnetic resonance imaging data of 21 non-psychotic individuals and 21 matched psychotic patients with frequent AVH were obtained. During scanning, subjects manually indicated the on- and offset of their AVH. Using independent component (IC) analysis, the data were split into 72 statistically independent spatial maps and their time courses. These time courses were regressed with the AVH time courses. With a one sample t-test on the beta weights, we selected those ICs that related to AVH in both groups for further dynamic functional network connectivity analysis. To identify functional connectivity states, k-means clustering was implemented on correlation matrices acquired using sliding windows. Group differences between these states were determined with two-sample t-tests.ResultsBoth groups experienced AVH during scanning, with a mean number of 24.71 AVH episodes in the clinical and 17.14 episodes in the non-clinical group. We identified seven ICs with time courses significantly related to the occurrence of AVH in both groups. The auditory, sensorimotor, and posterior salience network were positively related to AVH occurrence. The ventral default mode network (DMN), anterior salience network and a network consisting of (para-)hippocampal areas were negatively related to AVH.While in general, networks related to AVH were similar in both groups, a significant difference between the two groups was found in the mean dwell time in states characterized by varying connectivity between these networks. Psychotic patients spent more time in a state of low connectivity (r < 0.055) between all AVH-related networks. Non-psychotic patients dwelled longer in a different state, where some weak correlations between networks were present (.15 > r ≥ .10). Specifically, networks positively related to AVH showed small negative correlations with each other, and a small negative relationship with the DMN. At the same time, the anterior salience network displayed a small positive relationship with the sensorimotor, auditory and posterior salience networks.DiscussionOur findings suggest that similar brain networks underlie AVH in non-psychotic and psychotic individuals, but that the groups differ in terms of connectivity between those networks. Among the involved networks are those typically associated with AVH in psychotic patients, such as the DMN and auditory network. During the experience of AVH, psychotic individuals are more likely to show a state defined by segregation of the AVH-related networks. On the contrary, during AVH non-psychotic individuals are in a state defined by more connectivity between the networks. This suggests that a distinction between clinical and non-clinical AVH may have its neurobiological basis in the extend of disruption of involved network connectivity.

Large-Scale Neuronal Network Dysfunction in Diabetic Retinopathy.

Diabetic retinopathy (DR) patients are at an increased risk of cognitive decline and dementia. There is accumulating evidence that specific functional and structural architecture changes in the brain are related to cognitive impairment in DR patients. However, little is known regarding whether the functional architecture of resting-state networks (RSNs) changes in DR patients. The purpose of this study was to investigate the intranetwork functional connectivity (FC) and functional network connectivity (FNC) of RSN changes in DR patients using independent component analysis (ICA). Thirty-four DR patients (18 men and 16 women; mean age, 53.53 ± 8.67 years) and 38 nondiabetic healthy controls (HCs) (15 men and 23 women; mean age, 48.63 ± 11.83 years), closely matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. ICA was applied to extract the nine RSNs. Then, two-sample t-tests were conducted to investigate different intranetwork FCs within nine RSNs between the two groups. The FNC toolbox was used to assess interactions among RSNs. Pearson correlation analysis was conducted to explore the relationship between intranetwork FCs and clinical variables in the DR group. A receiver operating characteristic (ROC) curve was conducted to assess the ability of the intranetwork FCs of RSNs in discriminating between the two groups. Compared to the HC group, DR patients showed significant decreased intranetwork FCs within the basal ganglia network (BGN), visual network (VN), ventral default mode network (vDMN), right executive control network (rECN), salience network (SN), left executive control network (lECN), auditory network (AN), and dorsal default mode network (dDMN). In addition, FNC analysis showed increased VN-BGN, VN-vDMN, VN-dDMN, vDMN-lECN, SN-BGN, lECN-dDMN, and AN-BGN FNCs in the DR group, relative to the HC group. Furthermore, altered intranetwork FCs of RSNs were significantly correlated with the glycosylated hemoglobin (HbA1c) level in DR patients. A ROC curve showed that these specific intranetwork FCs of RSNs discriminated between the two groups with a high degree of sensitivity and specificity. Our study highlighted that DR patients had widespread deficits in both low-level perceptual and higher-order cognitive networks. Our results offer important insights into the neural mechanisms of visual loss and cognitive decline in DR patients.

Improvising at rest: Differentiating jazz and classical music training with resting state functional connectivity

Jazz improvisation offers a model for creative cognition, as it involves the real-time creation of a novel, information-rich product. Previous research has shown that when musicians improvise, they recruit regions in the Default Mode Network (DMN) and Executive Control Network (ECN). Here, we ask whether these findings from task-fMRI studies might extend to intrinsic differences in resting state functional connectivity. We compared Improvising musicians, Classical musicians, and Minimally Musically Trained (MMT) controls in seed-based functional connectivity and network analyses in resting state functional MRI. We also examined the functional correlates of behavioral performance in musical improvisation and divergent thinking. Seed-based analysis consistently showed higher connectivity in ventral DMN (vDMN) and bilateral ECN in both groups of musically trained individuals as compared to MMT controls, with additional group differences in primary visual network. In particular, primary visual network connectivity to DMN and ECN was highest in Improvisational musicians, as was connectivity between ECN and DMN; in contrast, connectivity between vDMN and frontal pole was highest in Classical musicians. Furthermore, graph-theoretical analysis indicated heightened network measures in both musician groups, with betweenness centrality, clustering, and local efficiency showing highest levels in Classical musicians, and degrees and strengths showing highest levels in Improvisational musicians. Taken together, results suggest that heightened functional connectivity among musicians can be explained by higher within-network connectivity (more tight-knit cortical networks) in Classical musicians, as opposed to more disperse, globally-connected cortical networks in Improvisational musicians.

Patterns of Cerebellar Connectivity with Intrinsic Connectivity Networks in Autism Spectrum Disorders.

There is growing evidence of altered connectivity in autism spectrum disorders (ASD) between the cerebellum and cortex. Three intrinsic connectivity networks (ICNs) are especially important to cognitive processing in ASD: the default mode network (DMN), executive control network (ECN), and salience networks (SNs). The goal of this study was to compare resting-state functional connectivity between the cerebellum and the DMN, ECN, and SN in ASD and typically developing children (n = 74, ages 7-12years). Children with ASD showed stronger connectivity between the ventral DMN and left cerebellar lobules I-IV. No meaningful relationships were observed between ICN-cerebellar functional connectivity and ASD symptoms. These results suggest that the cerebellum contributes to altered network connectivity in ASD.

Clinical Effects and Differences in Neural Function Connectivity Revealed by MRI in Subacute Hemispheric and Brainstem Infarction Patients With Dysphagia After Swallowing Therapy.

Background: Early detection and intervention for post-stroke dysphagia could reduce the incidence of pulmonary complications and mortality. The aims of this study were to investigate the benefits of swallowing therapy in swallowing function and brain neuro-plasticity and to explore the relationship between swallowing function recovery and neuroplasticity after swallowing therapy in cerebral and brainstem stroke patients with dysphagia.Methods: We collected 17 subacute stroke patients with dysphagia (11 cerebral stroke patients with a median age of 76 years and 6 brainstem stroke patients with a median age of 70 years). Each patient received swallowing therapies during hospitalization. For each patient, functional oral intake scale (FOIS), functional dysphagia scale (FDS) and 8-point penetration-aspiration scale (PAS) in videofluoroscopy swallowing study (VFSS), and brain functional magnetic resonance imaging (fMRI) were evaluated before and after treatment.Results: FOIS (p = 0.003 in hemispheric group and p = 0.039 in brainstem group) and FDS (p = 0.006 in hemispheric group and p = 0.028 in brainstem group) were both significantly improved after treatment in hemispheric and brainstem stroke patients. In hemispheric stroke patients, changes in FOIS were related to changes of functional brain connectivity in the ventral default mode network (vDMN) of the precuneus in brain functional MRI (fMRI). In brainstem stroke patients, changes in FOIS were related to changes of functional brain connectivity in the left sensorimotor network (LSMN) of the left postcentral region characterized by brain fMRI.Conclusion: Both hemispheric and brainstem stroke patients with different swallowing difficulties showed improvements after swallowing training. For these two dysphagic stroke groups with corresponding etiologies, swallowing therapy could contribute to different functional neuroplasticity.

Spontaneous Changes in Functional Connectivity of Independent Components of fMRI Signal in Healthy Volunteers at Rest and in Subjects with Mild Depression.

Depression is associated with changes in the pattern of interaction of cerebral networks, which can reflect both existing symptoms and compensatory processes. The study is based on analysis of resting state fMRI data from 15 patients with mild depression and 19 conventionally healthy individuals. From fMRI signal recorded at rest for 4 min, the independent components were reconstructed. The intergroup differences and dynamics of functional connectivity from the first to the second recording were analyzed. Initially, depressive patients demonstrated weaker connectivity between cerebellar declive network (CN) and left central executive network (CEN) and also sensorimotor network (SMN); left CEN and primary visual network (PVN). During the second recording, the patients demonstrated more intensive reciprocal connection of the dorsal domain of default mode network (DMN) and auditory network (AN). In healthy subjects, positive correlations of the dorsal DMN and left CEN, right CEN and CN, and negative correlation of dorsal DMN and visuospatial network weakened from the first to second record. In the depression group, the interaction of AN with PVN, the right CEN with the anterior salience network and with ventral DMN weakened. At the same time, the connectivity between SMN and CN were strengthened. The results can be interpreted as spontaneous normalization of brain activity, but no direct evidence for their relation to the improvement of depression symptoms was found.

Genetic interaction is associated with lower metabolic connectivity and memory impairment in clinically mild Alzheimer's disease.

Metabolic connectivity as showed by [18F] fluorodeoxyglucose (FDG) positron emission tomography (FDG-PET) reflects neuronal connectivity. The aim of this study was to investigate the genetic impact on metabolic connectivity in default mode subnetworks and its clinical-pathological relationships in patients with Alzheimer's disease (AD). We separately investigated the modulation of 2 default mode subnetworks, as identified with independent component analysis, by comparing APOE-ε4 carriers to noncarriers with AD. We further analyzed the interaction effects of APOE (APOE-ε4 carriers vs noncarriers) with PICALM (rs3851179-GG vs rs3851179-A-allele carriers) on episodic memory (EM) deficits, reduction in cerebral metabolic rate for glucose (CMRgl) and decreased metabolic connectivity in default mode subnetworks. The metabolic connectivity in the ventral default mode network (vDMN) was positively correlated with EM scores (β =0.441, P < .001). The APOE-ε4 carriers had significantly lower metabolic connectivity in the vDMN than the APOE-ε4 carriers (t(96) = -2.233, P = .028). There was an effect of the APOE-PICALM (rs3851179) interactions on reduced CMRgl in regions of vDMN (P < .001), and on memory deficits (F3,93 =5.568, P = .020). This study identified that PICALM may modulates memory deficits, reduced CMRgl and decreased metabolic connectivity in the vDMN in APOE-ε4 carriers. [18F] FDG-PET-based metabolic connectivity may serve a useful tool to elucidate the neural networks underlying clinical-pathological relationships in AD.

Dissociable effects of sleep deprivation on functional connectivity in the dorsal and ventral default mode networks

ObjectiveTo examine changes in functional connectivity of the default mode network (DMN) that are induced by sleep deprivation, and to identify individual differences that contribute to the vulnerability of the brain's response to sleep deprivation. MethodsUsing functional magnetic resonance imaging, we scanned 51 healthy young subjects during the resting state. Of these participants, 28 were scanned following 24 h of sleep deprivation, and 23 age- and education-matched control subjects were scanned after being well rested. ResultsIndependent component analysis was conducted to identify the DMN. Unlike previous studies that consider the DMN as one homogeneous network, the present study found a dissociable effect of sleep deprivation on two subsystems of the DMN. Functional connectivity within the dorsal DMN decreased; this was correlated with longer response times in a psychomotor vigilance task (PVT). An enhanced functional connectivity was found within the ventral DMN as well as between two subsystems, after sleep deprivation. In addition, between-subsystems connectivity was positively correlated with working memory and negatively correlated with the response time of PVT, suggesting a possible compensatory effect of enhanced communication across two subsystems. ConclusionsThe present findings suggest a dissociable effect of sleep deprivation on functional connectivity in the DMN. Lower functional connectivity in dorsal DMN was related to impairments of basic cognitive function. Notably, working memory was positively correlated with the putative compensatory enhanced functional connectivity across two subsystems, which in turn correlated with behavioral performance after sleep deprivation; this suggests that good working memory may play a protective role in sleep deprivation.

Beyond the limbic system: disruption and functional compensation of large-scale brain networks in patients with anti-LGI1 encephalitis

ObjectiveHippocampal inflammation in anti-LGI1 encephalitis causes memory deficits, seizures and behavioural abnormalities. Recent findings suggest that extralimbic brain areas are additionally affected and that patients also suffer from non-limbic cognitive...

Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson's disease.

Cognitive deficits in Parkinson's disease (PD) are heterogeneous entities, but a relationship between the heterogeneity of cognitive deficits and resting-state network (RSN) changes remains elusive. In this study, we examined five sub-domain scores according to Addenbrooke's Cognitive Examination-Revised (ACE-R) for the cognitive evaluation and classification of 72 non-demented patients with PD. Twenty-eight patients were classified as PD with normal cognition (PD-NC). The remaining 44 were subdivided into the following 2 groups using a hierarchical cluster analysis: 20 with a predominant decrease in memory (PD with amnestic cognitive deficits: PD-A) and 24 with good memory who exhibited a decrease in other sub-domains (PD with non-amnestic cognitive deficits: PD-NA). We used an independent component analysis of RS-fMRI data to investigate the inter-group differences of RSN. Compared to the controls, the PD-A showed lower FC within the ventral default mode network (vDMN) and the visuospatial network. On the other hand, the PD-NA showed lower FC within the visual networks and the cerebellum-brainstem network. Significant differences in the FC within the vDMN and cerebellum-brainstem network were observed between the PD-A and PD-NA, which provided a good discrimination between PD-A and PD-NA using a support vector machine. Distinct patterns of cognitive deficits correspond to different RSN changes.

DIFFERENTIAL EFFECT OF GLUCOSE METABOLISM AND INTRINSIC FUNCTIONAL CONNECTIVITY ON MEMORY PERFORMANCE OVER THE SPECTRUM OF ALZHEIMER'S DISEASE

While alterations in glucose metabolism are a well-established feature of Alzheimer's disease and linked to cognitive decline, aberrant patterns of spontaneous neural activity at rest are increasingly recognized as a characteristic of the disorder and are also evident in preclinical stages. The regional interrelationship of glucose consumption and resting-state activity and the differential contributions of these measures to memory function are still not well understood. The aim of the present study was to characterize this relationship and to assess the individual effects of the two modalities on memory function. Patients with subjective memory complaints (n=11), mild cognitive impairment (MCI) due to Alzheimer's disease (n=9), and early stage Alzheimer's dementia (n=10) were included in the analysis. We simultaneously acquired resting-state functional MRI (rs-fMRI) and [18F]fluorodeoxyglucose (FDG) PET data using a hybrid PET-MRI scanner. Independent component analysis was used to decompose rs-fMRI data into 75 spatially independent components of temporally synchronized neural activity. Using a combination of automated methods, we selected two default mode network components for the subsequent analysis. We performed voxel-wise regression analysis of intrinsic network connectivity and [18F]FDG uptake in the selected networks, correcting for voxelwise effects of gray matter volume. Mean values for both modalities were extracted from brain regions showing a significant effect of glucose consumption on intrinsic functional connectivity (p < 0.05 FWE-corrected) and entered into multiple regression models to estimate their effect on verbal memory performance (delayed recall of Logical Memory). Within the whole group, glucose uptake was significantly positively correlated with intrinsic connectivity in the ventral default mode network. Crucially, intrinsic connectivity but not glucose uptake predicted memory performance in patients with Alzheimer's disease (in the combined group of MCI and early stage dementia patients, as well in the early stage dementia group only). While glucose metabolism and intrinsic functional connectivity of resting state networks are closely interrelated, the disruption of functional connectivity in the default mode network better predicts memory performance. These results contribute to the development of rs-fMRI changes as a diagnostic marker and potential therapeutic target for Alzheimer's disease.

The correlation between mood states and functional connectivity within the default mode network can differentiate Internet gaming disorder from healthy controls

The default-mode network (DMN) has been suggested to support a baseline state of brain activity. However, whether connectivity within the DMN is associated with mood states remains incompletely understood. The current study examined the correlation between mood state and the functional connectivity (FC) among DMN regions, and examined if the FC can differentiate Internet gaming disorder (IGD) from healthy controls (HC). Resting state data were collected within 108 college students (IGD,41; HC,67). Negative correlations were observed between measures of: (1) Depression and FCs among ventral DMN regions; (2) Anger and FCs among dorsal DMN regions; and, (3) Anger and Depression and FCs of both the ventral and dorsal DMN. The results suggest that negative mood states of Depression and Anger might reflect poorer, or might impair, FCs among DMN regions. In addition, the FC among DMNs could be useful indexes in differencing IGD from HC. Future studies should examine the extent to which the findings may extend to clinical populations and whether increased connectivity of DMN regions may represent a mechanism for reducing negative mood states.