Functional Network Activity Research Articles

The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention tool for the noninvasive modulation of brain activity and behavior in neuroscience research and clinical settings. However, the resting-state dynamic evolution of large-scale functional brain networks following rTMS has rarely been investigated. Here, using resting-state fMRI images collected from 23 healthy individuals before (baseline) and after 1 Hz rTMS of the left frontal (FRO) and occipital (OCC) lobes, we examined the different effects of rTMS on brain dynamics across the human cortex. By fitting a pairwise maximum entropy model (pMEM), we constructed an energy landscape for the baseline and poststimulus conditions by fitting a pMEM. We defined dominant brain states (local minima) in the energy landscape with synergistic activation and deactivation patterns of large-scale functional networks. We calculated state dynamics including appearance probability, transitions and duration. The results showed that 1 Hz rTMS induced increased and decreased state probability, transitions and duration when delivered to the FRO and OCC targets, respectively. Most importantly, the shortest path and minimum cost between dominant brain states were altered after stimulation. The absolute sum of the costs from the source states to the destinations was lower after OCC stimulation than after FRO stimulation. In conclusion, our study characterized the dynamic trajectory of state transitions in the energy landscape and suggested that local rTMS can induce significant dynamic perturbation involving stimulated and distant functional networks, which aligns with the modern view of the dynamic and complex brain. Our results suggest low-dimensional mapping of rTMS-induced brain adaption, which will contribute to a broader and more effective application of rTMS in clinical settings.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia.

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Peripheral and central pathogenesis of postherpetic neuralgia.

Postherpetic neuralgia (PHN) is a classic chronic condition with multiple signs of peripheral and central neuropathy. Unfortunately, the pathogenesis of PHN is not well defined, limiting clinical treatment and disease management. To describe the peripheral and central pathological axes of PHN, including peripheral nerve injury, inflammation induction, central nervous system sensitization, and brain functional and structural network activity. A bibliographic survey was carried out, selecting relevant articles that evaluated the characterization of the pathogenesis of PHN, including peripheral and central pathological axes. Currently, due to the complexity of the pathophysiological mechanisms of PHN and the incomplete understanding of the exact mechanism of neuralgia. It is essential to conduct in-depth research to clarify the origins of PHN pathogenesis and explore effective and comprehensive therapies for PHN.

Pathological and neurochemical correlates of locus coeruleus functional network activity

The locus coeruleus (LC) produces the neuromodulators norepinephrine and dopamine, and projects widely to subcortical and cortical brain regions. The LC has been a focus of neuroimaging biomarker development for the early detection of Alzheimer’s disease (AD) since it was identified as one of the earliest brain regions to develop tau pathology. Our recent research established the use of positron emission tomography (PET) to measure LC catecholamine synthesis capacity in cognitively unimpaired older adults. We extend this work by investigating the possible influence of pathology and LC neurochemical function on LC network activity using functional magnetic resonance imaging (fMRI). In separate sessions, participants underwent PET imaging to measure LC catecholamine synthesis capacity ([18F]Fluoro-m-tyrosine), tau pathology ([18F]Flortaucipir), and amyloid-β pathology ([11C]Pittsburgh compound B), and fMRI imaging to measure LC functional network activity at rest. Consistent with a growing body of research in aging and preclinical AD, we find that higher functional network activity is associated with higher tau burden in individuals at risk of developing AD (amyloid-β positive). Critically, relationships between higher LC network activity and higher pathology (amyloid-β and tau) were moderated by LC catecholamine synthesis capacity. High levels of LC catecholamine synthesis capacity reduced relationships between higher network activity and pathology. Broadly, these findings support the view that individual differences in functional network activity are shaped by interactions between pathology and neuromodulator function, and point to catecholamine systems as potential therapeutic targets.

Abnormal functional connectivity of white-matter networks and gray-white matter functional networks in patients with NMOSD

Cognitive impairment (CI) has been reported in 29–70% of patients with neuromyelitis optica spectrum disorder (NMOSD). Abnormal white matter (WM) functional networks that correlate with cognitive functions have not been studied well in patients with NMOSD. The aim of the current study was to investigate functional connectivity (FC), spontaneous activity, and functional covariance connectivity (FCC) abnormalities of WM functional networks in patients with NMOSD and their correlation with cognitive performance. Twenty-four patients with NMOSD and 24 healthy controls (HCs) were included in the study. Participants underwent brain resting-state functional magnetic resonance imaging (fMRI) and the Montreal Cognitive Assessment (MoCA). Eight WM networks and nine gray matter (GM) networks were created. In patients, WM networks, including WM1–4, WM1–8, WM2–6, WM2–7, WM2–8, WM4–8, WM5–8 showed reduced FC (P < 0.05). All WM networks except WM1 showed decreased spontaneous activity (P < 0.05). The major GM networks demonstrated increased/decreased FC (P < 0.05), whereas GM7-WM7, GM8-WM4, GM8-WM6 and GM8-WM8 displayed decreased FC (P < 0.05). The MoCA results showed that two-thirds (16/24) of the patients had CI. FC and FCC in WM networks were correlated negatively with the MoCA scores (P < 0.05). WM functional networks are multi-layered. Abnormal FC of WM functional networks and GM functional networks may be responsible for CI.

Resting State Brain Networks under Inverse Agonist versus Complete Knockout of the Cannabinoid Receptor 1.

The cannabinoid receptor 1 (CB1) is famous as the target of Δ9-tetrahydrocannabinol (THC), which is the active ingredient of marijuana. Suppression of CB1 is frequently suggested as a drug target or gene therapy for many conditions (e.g., obesity, Parkinson's disease). However, brain networks affected by CB1 remain elusive, and unanticipated psychological effects in a clinical trial had dire consequences. To better understand the whole brain effects of CB1 suppression we performed in vivo imaging on mice under complete knockout of the gene for CB1 (cnr1-/-) and also under the CB1 inverse agonist rimonabant. We examined white matter structural changes and brain function (network activity and directional uniformity) in cnr1-/- mice. In cnr1-/- mice, white matter (in both sexes) and functional directional uniformity (in male mice) were altered across the brain but network activity was largely unaltered. Conversely, under rimonabant, functional directional uniformity was not altered but network activity was altered in cortical regions, primarily in networks known to be altered by THC (e.g., neocortex, hippocampal formation). However, rimonabant did not alter many brain regions found in both our cnr1-/- results and previous behavioral studies of cnr1-/- mice (e.g., thalamus, infralimbic area). This suggests that chronic loss of cnr1 is substantially different from short-term suppression, subtly rewiring the brain but largely maintaining the network activity. Our results help explain why pathological mutations in CB1 (e.g., chronic pain) do not always provide insight into the side effects of CB1 suppression (e.g., clinical depression), and thus urge more preclinical studies for any drugs that suppress CB1.

Nedd4-2-dependent regulation of astrocytic Kir4.1 and Connexin43 controls neuronal network activity.

Nedd4-2 is an E3 ubiquitin ligase in which missense mutation is related to familial epilepsy, indicating its critical role in regulating neuronal network activity. However, Nedd4-2 substrates involved in neuronal network function have yet to be identified. Using mouse lines lacking Nedd4-1 and Nedd4-2, we identified astrocytic channel proteins inwardly rectifying K+ channel 4.1 (Kir4.1) and Connexin43 as Nedd4-2 substrates. We found that the expression of Kir4.1 and Connexin43 is increased upon conditional deletion of Nedd4-2 in astrocytes, leading to an elevation of astrocytic membrane ion permeability and gap junction activity, with a consequent reduction of γ-oscillatory neuronal network activity. Interestingly, our biochemical data demonstrate that missense mutations found in familial epileptic patients produce gain-of-function of the Nedd4-2 gene product. Our data reveal a process of coordinated astrocytic ion channel proteostasis that controls astrocyte function and astrocyte-dependent neuronal network activity and elucidate a potential mechanism by which aberrant Nedd4-2 function leads to epilepsy.

Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest.

Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short-lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.

Alteration of the large-scale white-matter functional networks in autism spectrum disorder.

Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.

Dynamic functional connectivity MEG features of Alzheimer’s disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer’s disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer’s disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

Variation in functional networks between clinical and subclinical discharges in childhood absence epilepsy: A multi-frequency MEG study

ObjectiveTwo types of spike-and-wave discharges (SWDs) exist in childhood absence epilepsy (CAE): clinical discharges are prolonged and manifest primarily as impaired consciousness, whereas subclinical discharges are brief with few objectively visible symptoms. This study aimed to compare neural functional network and default mode network (DMN) activity between clinical and subclinical discharges to better understand the underlying mechanism of CAE. MethodsUsing magnetoencephalography (MEG) data from 21 patients, we obtained 25 segments each of clinical discharges and subclinical discharges. Amplitude envelope correlation analysis was used to construct functional networks and graph theory was used to calculate network topological data. We then compared differences in functional connectivity within the DMN between clinical and subclinical discharges. All statistical comparisons were performed using paired-sample tests. ResultsCompared to subclinical discharges, the functional network of clinical discharges exhibited higher synchronization — particularly in the parahippocampal gyrus — as early as 10 s before the seizure. Additionally, the functional network of clinical SWDs presented an anterior shift of key nodes in the alpha frequency band. Regarding clinical discharge progression, there were gradual increases in the parameter node strengths (S), clustering coefficients (C), and global efficiency (E) of the functional networks, while the path lengths (L) decreased. These changes were most prominent at the onset of discharges and followed by some recovery in the high-frequency bands, but no significant change in the low-frequency bands. Furthermore, connections within the DMN during the discharge period were significantly stronger for clinical discharge compared to subclinical discharges. ConclusionsThese findings suggest that a more regular network before abnormal discharges in clinical discharges contributes to SWD explosion and that the parahippocampal gyrus plays an important role in maintaining oscillations. An absence seizure is a gradual process and the emergence of SWDs may be accompanied by initiation of inhibitory mechanisms. Enhanced functional connectivity among DMN brain regions may indicate that patients have entered a state of introspection, and functional abnormalities in the parahippocampal gyrus may be associated with patients' transient memory loss.

Echoes from Intrinsic Connectivity Networks in the Subcortex.

Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity.

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Disturbance of functional brain networks and cognitive decline in Parkinson's disease: Severe cerebral small vessel disease aggravates this relationship

IntroductionSeveral studies have identified a relationship between functional brain network disturbance and cognitive decline in people with Parkinson's disease (PwP); however, few studies have explored whether cerebral small vessel disease (CSVD) burden modifies this relationship. This study aimed to investigate the potential moderating effect of CSVD on the relationship between functional brain network disturbance and cognitive decline in PwP. MethodsWe prospectively recruited 61 PwP from Beijing Tiantan Hospital between October 2021 to September 2022. The Montreal Cognitive Assessment (MoCA) score was used to assess cognition. CSVD imaging markers were evaluated following the STandards for ReportIng Vascular changes on nEuroimaging instructions, and the CSVD burden score was calculated. The functional connectivity indicator was obtained and calculated using quantitative electroencephalography examination. The moderating effect of CSVD burden on the relationship between functional brain network disturbance and cognitive decline was examined using hierarchical linear regression. ResultsForty-six of 61 (75.4%) PwP had cognitive impairment. Higher global weighted phase lag index (wPLI) values in beta1 bands were significantly associated with lower adjusted MoCA scores. CSVD burden aggravated the effect of the global wPLI in beta1 bands on adjusted MoCA scores. This effect was reinforced by the high level of CSVD burden. ConclusionsHigher wPLI indicates a possible pathological activation of functional brain networks that are associated with cognitive decline in PwP, and the high level of CSVD burden aggravates this relationship.

Dynamics of Cerebral Function in Patients with Acute Cerebellar Infarction.

Few studies were devoted to investigating cerebral functional changes after acute cerebellar infarction (CI). The purpose of this study was to examine the brain functional dynamics of CI using electroencephalographic (EEG) microstate analysis. And the possible heterogenicity in neural dynamics between CI with vertigo and CI with dizziness was explored. Thirty-four CI patients and 37 age- and gender-matched healthy controls(HC) were included in the study. Each included subject underwent a 19-channel video EEG examination. Five 10-s resting-state EEG epochs were extracted after data preprocessing. Then, microstate analysis and source localization were performed using the LORETA-KEY tool. Microstate parameters such as duration, coverage, occurrence, and transition probability are all extracted. The current study showed that the duration, coverage, and occurrence of microstate(Ms) B significantly increased in CI patients, but the duration and coverage of MsA and MsD decreased. Compared CI with vertigo to dizziness, finding a decreasedtrend in the coverage of MsD and the transition from MsA and MsB to MsD. Taken together, our study sheds new light on the dynamics of cerebral function after CI, mainly reflecting increased activity in functional networks involved in MsB and decreased activity in functional networks involved in MsA and MsD. Vertigo and dizziness post-CI may be suggested by cerebral functional dynamics. Further longitudinal studies are needed to validate and explore the alterations in brain dynamics to what extent depict the clinical traits and their potential applications in the recovery of CI.

Energy-efficient network activity from disparate circuit parameters

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

Cerebral cortex Functional Networks of Transdermal Electrical Stimulation at Daling (PC7) Acupoint.

The cerebral cortex functional network of Electroencephalogram (EEG) signals during transcutaneous electrical acupoint stimulation (TEAS) on 21 healthy subjects was constructed by using three modules: standard low-resolution brain electromagnetic tomography (sLORETA), phase-locking value (PLV), and complex network. We investigated the brain functional network triggered by PC7 stimulation by comparing with resting state and non-acupoint stimulation. The results showed that the PC7 stimulation mainly activated frontal lobe and temporal lobe including prefrontal cortex (BA10), insular lobe (BA13), temporal gyrus (BA22), anterior cingulate cortex (BA32), temporal pole (BA38), dorsolateral prefrontal cortex (BA46), and inferior frontal cortex (BA47), which are all closely linked to cognition, spirit, and emotion in brain. Furthermore, the degrees of node in frontal, temporal, and whole brain are increased significantly or extreme significantly with p < 0.05, p < 0.05, and p < 0.01, respectively; clustering coefficient in frontal, temporal, and whole brain are all statistically significant (p < 0.05). The information transmission efficiency of cerebral cortex has been greatly improved. During PC7 stimulation, the topological changes in the activation of cerebral regions and cortical functional networks are consistent with the therapeutic effect, which may provide theoretical support for acupoint stimulation to regulate nerve function.

Altered activity in functional brain networks involved in lexical decision making in bipolar disorder: An fMRI case-control study

BackgroundBrain networks involved in language, attentional and response processes are detectable by fMRI during lexical decision (LD). Here, we investigated possible abnormalities in the functional networks involved in LD in patients with bipolar disorder (BD). MethodsfMRI and behavioural data were compared between BD (n = 25) and control (n = 21), with groups matched for age and sex. The functional brain networks involved in LD were extracted by manipulating the “word-likeness” of LD stimuli and using a multidimensional analysis method. ResultsAttentional, response and language processes were captured in separate function-specific brain networks (default mode network, response network, linguistic processing network, respectively) in the BD and control groups, replicating the results of our previous study in an independent group of healthy adults. Behaviourally, the BD group showed higher performance than the control group in the LD task. Activity in the default mode network (DMN) and the linguistic processing network (LPN) did not differ between the groups, but the BD group had higher activation than the control group in the response network (RESP). LimitationsDue to the small sample, the study is underpowered, capable of only detecting large effects. ConclusionsThe results suggest that BD may be associated with sustained activity in the RESP network, which might contribute to psychomotor dysfunction in BD. Future studies should investigate the possible link between altered RESP activation and psychomotor disturbances in BD, as well as the basis for altered RESP activity in BD.

Functional Connectivity as an Index of Brain Changes Following a Unicycle Intervention: A Graph-Theoretical Network Analysis.

Grey matter volume reductions in the right superior temporal gyrus (rSTG) were observed in young adults who learned to ride a unicycle. As these decreases were correlated with the acquired ability in unicycling, the authors interpreted the change as a brain tissue reorganization to increase postural control’s automated and efficient coordination. The current study aims to further corroborate this interpretation by looking at changes in the functional brain network in the very same sample of participants. For this reason, we applied graph theory, a mathematics field used to study network structure functionality. Four global and two local graph-theoretical parameters were calculated to measure whole brain and rSTG specific changes in functional network activity following the three-week-unicycle training. Findings revealed that the Local Efficiency of the rSTG was significantly elevated after the intervention indicating an increase in fault tolerance of the rSTG, possibly reflecting decentralisation of rSTG specific functions to neighbouring nodes. Thus, the increased Local Efficiency may indicate increased task efficiency by decentralising the processing of functions crucial for balance.