Default Mode Network Network Research Articles

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment.

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning.

Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA). Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups. In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292. In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.

Abnormal large-scale resting-state functional networks in anti-N-methyl-D-aspartate receptor encephalitis.

Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis.

Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective.

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Altered regional homogeneity following moxibustion in mild cognitive impairment.

Moxibustion has a definite clinical effect in improving the cognitive condition in individuals with mild cognitive impairment (MCI), but its underlying neural mechanism remains elusive. This study aimed to investigate the alterations in spontaneous brain activity and cognitive function following moxibustion therapy in MCI patients. This study enrolled a cohort of 33 MCI subjects and 30 matched healthy controls (HCs). MCI subjects underwent a two-month regimen of moxibustion. Employing resting-state functional magnetic resonance imaging, we utilized regional homogeneity (ReHo) analysis to evaluate the changes in brain activity. Cognitive function was evaluated by using the Mini-Mental State Examination and Montreal Cognitive Assessment. There existed aberrant ReHo values in different brain areas mainly involved in the default mode network (DMN) in MCI subjects compared with HCs. After moxibustion treatment, MCI subjects showed an inverse in ReHo values from baseline in the hippocampus/parahippocampus and insula, as well as an increase inReHo value in the middle frontal gyrus. Notably, the ReHo alterations in the left hippocampus/parahippocampus and middle frontal gyrus were associated with cognitive improvement in MCI patients. Abnormal neural activity occurred in MCI subjects mainly within the DMN. Moxibustion therapy may facilitate cognitive improvement in MCI subjects by modulating brain activity, particularly by reversing the neural activity within the DMN and salience network. These results underscore the therapeutic potential of moxibustion as an early intervention strategy for Alzheimer's disease.

An examination of resting-state functional connectivity in patients with active Crohn's disease.

Alterations in resting state functional connectivity (rs-FC) in Crohn's Disease (CD) have been documented in default mode network (DMN) and frontal parietal network (FPN) areas, visual, cerebellar, salience and attention resting-state-networks (RSNs), constituting a CD specific neural phenotype. To date, most studies are in patients in remission, with limited studies in active disease. Twenty five active CD cases and 25 age-, BMI- and gender-matched healthy controls (HC) were recruited to a resting-state-functional Magnetic Resonance Imaging (rs-fMRI) study. Active disease was defined as C-reactive protein>5 mg/dL, faecal calprotectin>250 μg/g, or through ileocolonoscopy or MRE. rs-fMRI data were analysed using independent component analysis (ICA) and dual regression. Differences in RSNs between HCs and active CD were assessed, and rs-FC was associated with disease duration and abdominal pain. Increased connectivity in the FPN (fusiform gyrus, thalamus, caudate, posterior cingulate cortex, postcentral gyrus) and visual RSN (orbital frontal cortex) were observed in CD versus HC. Decreased activity was observed in the salience network (cerebellum, postcentral gyrus), DMN (parahippocampal gyrus, cerebellum), and cerebellar network (occipital fusiform gyrus, cerebellum) in CD versus HCs. Greater abdominal pain scores were associated with lower connectivity in the precuneus (visual network) and parietal operculum (salience network), and higher connectivity in the cerebellum (frontal network). Greater disease duration was associated with greater connectivity in the middle temporal gyrus and planum temporale (visual network). Alterations in rs-FC in active CD in RSNs implicated in cognition, attention, emotion, and pain may represent neural correlates of chronic systemic inflammation, abdominal pain, disease duration, and severity.

Relationship between brain structural network integrity and emotional symptoms in youth with perinatally-acquired HIV

Perinatally acquired HIV infection (PHIV) currently affects approximately 1.7 million children worldwide. Youth with PHIV (YPHIV) are at increased risk for emotional and behavioral symptoms, yet few studies have examined relationships between these symptoms and brain structure. Previous neuroimaging studies in YPHIV report alterations within the salience network (SN), cognitive control network (CCN), and default mode network (DMN). These areas have been associated with social and emotional processing, emotion regulation, and executive function. We examined structural brain network integrity from MRI using morphometric similarity networks and graph theoretical measures of segregation (transitivity), resilience (assortativity), and integration (global efficiency). We examined brain network integrity of 40 YPHIV compared to 214 youths without HIV exposure or infection. Amongst YPHIV, we related structural brain network metrics to the Emotional Symptoms Index of the Behavioral Assessment System for Children, 2nd edition. We also examined the relationship of inflammatory biomarkers in YPHIV to brain network integrity. YPHIV had significantly lower global efficiency in the SN, DMN, and the whole brain network compared to controls. YPHIV also demonstrated lower assortativity or resilience (i.e., network robustness) compared to controls in the DMN and whole brain network. Further, higher emotional symptom score was associated with higher global efficiency in the SN and lower global efficiency in the DMN, signaling more emotional challenges. A significant association was also found between several inflammatory and cardiac markers with structural network integrity. These findings suggest an impact of HIV on developing brain networks, and potential dysfunction of the SN and DMN in relation to network efficiency.

Underlying mechanisms, clinical manifestation, and methodological considerations of functional connectivity in Dementia with Lewy Bodies

AbstractBackgroundFunctional connectivity disturbances are potential disease mechanisms related to observable symptoms in Dementia with Lewy Bodies (DLB). Despite several existing theories, the exact relationship between functional connectivity, underlying mechanisms of the DLB, and symptomatology is yet not clear. The present review addresses this gap by summarising research findings of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) research while considering the impact of different methodological choices causing mixed findings.MethodWe performed a systematic literature review of resting‐state functional connectivity in DLB following PRISMA. We searched multiple databases from January 2000 to November 2022. Keywords included “DLB”, “connectivity”, “EEG”, and “fMRI”, their synonyms and acronyms, and related terms of network analysis. The final sample contained 6 EEG and 14 fMRI studies.ResultThe main methodological choices of fMRI articles included a data‐driven approach (i.e., Independent Component Analysis or Graph Theory; 43%), seed‐based or regions of interest (50%), or their combination (7%) with most studies focusing on large‐scale resting‐state networks (71%). The overall trend of connectivity findings showed a decrease, yet more subtle changes in the directionality of connectivity were observed when using a hypothesis‐driven approach (Fig 1A). The Default Mode Network (DMN) was the most studied network with 75% of relevant articles reporting decreased connectivity. Other networks with a consistent decrease in connectivity included Executive and Sensorimotor Networks (80% and 100% of relevant studies respectively). The inter‐network connectivity revealed the mixed relationship between the DMN, executive, attentional and visual networks (VN; Fig 1B). EEG studies showed a consistent decrease in connectivity on alpha and beta bands (100% and 66% of relevant studies respectively) and disorganisation in graph properties in regions overlapping with findings from large‐scale networks.ConclusionDysfunctions in large‐scale networks likely contribute to progressive degenerative mechanisms and are consistent with visual attention and perception processing theories of psychotic symptoms in the DLB. Hence, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering underlying relationships between the networks’ disorganisation and disease mechanisms resulting in psychotic, motor, and cognitive symptoms in DLB. The choice of analytical approach might contribute to the directionality and mixed findings in the literature.

Anterior and posterior default mode network functional connectivity and segregation in aging

AbstractBackgroundThe default mode network (DMN) supports memory performance and has been shown to be vulnerable to the effects of aging. The posterior DMN (pDMN) is an early region of amyloid‐β (Aβ) accumulation, which may lead to disrupted communication within/between subnetworks of the DMN. Using resting state functional connectivity (FC), we tested whether communication within and between the pDMN and anterior DMN (aDMN) were altered by aging and Aβ pathology and related to memory performance. We also tested segregation FC between the aDMN and pDMN.MethodData from 27 cognitively normal adults (69.6 years, 59% females) with resting state fMRI (rsfMRI), 18F‐florbetapir (FBP) PET to measure Aβ, and the Rey Auditory Verbal LearningTest (RAVLT) delayed recall to assess memory were analyzed. The rsfMRI data were preprocessed using CONN toolbox. ROI‐to‐ROI FC matrices were constructed for regions included in the aDMN and pDMN. We calculated: (1) the average FC within each network (Zw), (2) average FC between the aDMN and pDMN (Zb), and (3) network segregation [(Zw‐Zb)/Zw] for each DMN network. Global FBP SUVR was calculated as the mean across association cortical regions, and a cutoff of 1.11 was used to determine Aβ positivity. Analyses included the FC variables and their associations with age, Aβ, and memory.ResultOlder age was associated with reduced network segregation of the pDMN (r = ‐0.53, p = 0.004), and a trend level association for reduced within‐pDMN FC (r = ‐0.37, p = 0.057). Global FBP SUVR was not associated with FC or network segregation, and there was no significant difference in FC between Aβ groups (controlling for age). There was a trend‐level association between better memory performance and stronger FC between aDMN and pDMN (r = 0.37, p = 0.066; controlling for age and education).ConclusionResults demonstrated that aging is associated with specific FC disruptions of pDMN, leading to reduced specialization of processing as indexed by network segregation. Moreover, increased communication between DMN subnetworks may support memory in aging. Future analyses in a larger sample will further test the association between Alzheimer’s pathology and functional changes to DMN subnetworks.

Cognitive impairment after sleep deprivation: The role of precuneus related connectivity on the intra-individual variability changes

ObjectiveIntra-individual variability (IIV) in cognitive performance is thought to reflect the efficiency with which attentional resources are allocated in different circumstances requiring cognitive control. IIV in cognitive performance is associated with the strength of the negative correlation between task-positive network and default mode network (DMN) activity. In this study, we investigated the impact of sleep deprivation (SD) on functional connectivity (FC) between the DMN and psychomotor vigilance task-related network (PVT-RN), and its relationship with IIV in cognitive performance. MethodsTwo analyses, network-level independent component analysis (NL-ICA) and region-level (RL)-ICA, were employed to compare the coefficient of variation (CV) of the PVT between normal sleep and SD conditions across 67 healthy participants. ResultsAfter SD, in NL-ICA, the FC between the PVT-RN and DMN was positively correlated with the CV of the PVT, as well as the changes therein, compared with normal sleep. Using a mask derived from the DMN and PVT-RN, the RL-ICA revealed that 12 edges/connections between DMN and PVT independent components were associated with the CV of the PVT, with nine of these connections involving the precuneus. ConclusionsThese findings suggest that the precuneus may play a crucial role in the interactions of various brain functions during the PVT, with the connections between the precuneus and frontoparietal and somatosensory networks being significantly altered after SD. Moreover, following SD, weakened negative FC between the precuneus and bilateral inferior parietal lobule may disrupt the balance between cognitive and executive control functions, leading to a decline in cognitive performance.

Static and dynamic functional connectivity combined with the triple network model in amnestic mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders. Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately. Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores. It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.

Impact of Yoga Versus Memory Enhancement Training on Hippocampal Connectivity in Older Women at Risk for Alzheimer's Disease.

Yoga may be an ideal early intervention for those with modifiable risk factors for Alzheimer's disease (AD) development. To examine the effects of Kundalini yoga (KY) training versus memory enhancement training (MET) on the resting-state connectivity of hippocampal subregions in women with subjective memory decline and cardiovascular risk factors for AD. Participants comprised women with subjective memory decline and cardiovascular risk factors who participated in a parent randomized controlled trial (NCT03503669) of 12-weeks of KY versus MET and completed pre- and post-intervention resting-state magnetic resonance imaging scans (yoga: n = 11, age = 61.45±6.58 years; MET: n = 11, age = 64.55±6.41 years). Group differences in parcellated (Cole-anticevic atlas) hippocampal connectivity changes (post- minus pre-intervention) were evaluated by partial least squares analysis, controlling for age. Correlations between hippocampal connectivity and perceived stress and frequency of forgetting (assessed by questionnaires) were also evaluated. A left anterior hippocampal subregion assigned to the default mode network (DMN) in the Cole-anticevic atlas showed greater increases in connectivity with largely ventral visual stream regions with KY than with MET (p < 0.001), which showed associations with lower stress (p < 0.05). Several posterior hippocampal subregions assigned to sensory-based networks in the Cole-anticevic atlas showed greater increases in connectivity with regions largely in the DMN and frontoparietal network with MET than with KY (p < 0.001), which showed associations with lower frequency of forgetting (p < 0.05). KY training may better target stress-related hippocampal connectivity, whereas MET may better target hippocampal sensory-integration supporting better memory reliability, in women with subjective memory decline and cardiovascular risk factors.

Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease.

Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13–18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Similarities and differences between intermittent and continuous resting-state fMRI.

Functional Magnetic Resonance Imaging (fMRI) block-design experiments typically include active ON-blocks with presentation of cognitive tasks which are contrasted with OFF- blocks with no tasks presented. OFF-blocks in between ON-blocks can however, also be seen as a proxy for intermittent periods of resting, inducing temporary resting-states. We still do not know if brain activity during such intermittent periods reflects the same kind of resting-state activity as that obtained during a continuous period, as is typically the case in studies of the classic Default Mode Network (DMN). The purpose of the current study was therefore to investigate both similarities and differences in brain activity between intermittent and continuous resting conditions. There were 47 healthy participants in the 3T fMRI experiment. Data for the intermittent resting-state condition were acquired from resting-periods in between active task-processing periods in a standard ON-OFF block design, with three different cognitive tasks presented during ON-blocks. Data for the continuous resting-state condition were acquired during a 5 min resting period after the task-design had been presented. The results showed that activity was overall similar in the two conditions, but with some differences. These differences were within the DMN network, and for the interaction of DMN with other brain networks. DMN maps showed weak overlap between conditions in the medial prefrontal cortex (MPFC), and in particular for the intermittent compared to the continuous resting-state condition. Moreover, DMN showed strong connectivity with the salience network (SN) in the intermittent resting-state condition, particularly in the anterior insula and the supramarginal gyrus. The observed differences may reflect a "carry-over" effect from task-processing to the next resting-state period, not present in the continuous resting-state condition, causing interference from the ON-blocks. Further research is needed to fully understand the extent of differences between intermittent and continuous resting-state conditions.

Functional and structural networks decoupling in generalized tonic-clonic seizures and its reorganization by drugs.

To investigate potential functional and structural large-scale network disturbances in untreated patients with generalized tonic-clonic seizures (GTCS) and the effects of antiseizure drugs. In this study, 41 patients with GTCS, comprising 21 untreated patients and 20 patients who received antiseizure medications (ASMs), and 29 healthy controls were recruited to construct large-scale brain networks based on resting-state functional magnetic resonance imaging and diffusion tensor imaging. Structural and functional connectivity and network-level weighted correlation probability (NWCP) were further investigated to identify network features that corresponded to response to ASMs. Untreated patients showed more extensive enhancement of functional and structural connections than controls. Specifically, we observed abnormally enhanced connections between the default mode network (DMN) and the frontal-parietal network. In addition, treated patients showed similar functional connection strength to that of the control group. However, all patients exhibited similar structural network alterations. Moreover, the NWCP value was lower for connections within the DMN and between the DMN and other networks in the untreated patients; receiving ASMs could reverse this pattern. Our study identified alterations in structural and functional connectivity in patients with GTCS. The influence of ASMs may be more noticeable within the functional network; moreover, abnormalities in both the functional and structural coupling state may be improved by ASM treatment. Therefore, the coupling state of structural and functional connectivity may be used as an indicator of the efficacy of ASMs.

Pharmacological treatment-associated brain structural and functional alterations in major depressive disorder: A narrative review

Major depressive disorder (MDD) is a complicated mental disorder with an unclear etiology and a relatively limited response to treatment. Magnetic resonance imaging (MRI) can help identify the underlying neuropathophysiological mechanisms of MDD. A better understanding of structural and functional alterations in the brain before and after treatment is necessary to improve treatment targets at the brain level. This narrative review summarizes the literature on longitudinal MRI studies in MDD, which identify brain regions with changes in neural activity after antidepressant treatment, providing an objective imaging basis for early diagnosis and assessment of the efficacy of antidepressant therapy. It was found that the neural activity in the prefrontal cortex, cingulate cortex, precuneus, and hippocampus was reduced in patients with untreated MDD. In addition, functional connectivity of the default mode network (DMN), central executive, and salience networks was enhanced, gray matter volume was reduced, and white matter structure was damaged. After antidepressant treatment, neural activity in some brain regions increased and functional connectivity of brain networks and gray and white matter structures returned to normal states. Although the results have been inconsistent, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli are promising predictors of successful treatment, and hyperconnectivity within and between the DMN and executive control network exhibit similar results.

Planning ahead: Predictable switching recruits task-active and resting-state networks.

Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.

The body mass index is associated with increased temporal variability of functional connectivity in brain reward system.

The reward system has been proven to be contributed to the vulnerability of obesity. Previous fMRI studies have shown abnormal functional connectivity of the reward system in obesity. However, most studies were based on static index such as resting-state functional connectivity (FC), ignoring the dynamic changes over time. To investigate the dynamic neural correlates of obesity susceptibility, we used a large, demographically well-characterized sample from the Human Connectome Project (HCP) to determine the relationship of body mass index (BMI) with the temporal variability of FC from integrated multilevel perspectives, i.e., regional and within- and between-network levels. Linear regression analysis was used to investigate the association between BMI and temporal variability of FC, adjusting for covariates of no interest. We found that BMI was positively associated with regional FC variability in reward regions, such as the ventral orbitofrontal cortex and visual regions. At the intra-network level, BMI was positively related to the variability of FC within the limbic network (LN) and default mode network (DMN). At the inter-network level, variability of connectivity of LN with DMN, frontoparietal, sensorimotor, and ventral attention networks showed positive correlations with BMI. These findings provided novel evidence for abnormal dynamic functional interaction between the reward network and the rest of the brain in obesity, suggesting a more unstable state and over-frequent interaction of the reward network and other attention and cognitive networks. These findings, thus, provide novel insight into obesity interventions that need to decrease the dynamic interaction between reward networks and other brain networks through behavioral treatment and neural modulation.

Amyloid-β accumulation in relation to functional connectivity in aging: A longitudinal study

The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n = 300) and explored possible relationships between amyloid beta (Aβ) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aβ accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions. Each participant had at least 2 to 3 follow-up visits. A linear mixed-effect model was used to examine longitudinal changes of Aβ accumulation and FC throughout the whole brain. We found that the limbic and frontoparietal networks had a greater annual Aβ accumulation and a slower decline in FC in aging. Additionally, the amount of the Aβ deposition in the amygdala network at baseline slowed down the decline in its FC in aging. Furthermore, the functional connectivity of the limbic, default mode network (DMN), and frontoparietal networks accelerated the Aβ propagation across their functionally highly connected regions. The functional connectivity of the somatomotor and visual networks accelerated the Aβ propagation across the brain regions in the limbic, frontoparietal, and DMN networks. These findings suggested that the slower decline in the functional connectivity of the functional hubs may compensate for their greater Aβ accumulation in aging. The Aβ propagation from one brain region to the other may depend on their functional connectivity strength.