Similar Functional Connectivity Research Articles

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

Spectral density-based clustering algorithms for complex networks.

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are interested in grouping networks with similar connectivity structures together instead of grouping vertices of the graph. We could apply this approach to functional brain networks (FBNs) for identifying subgroups of people presenting similar functional connectivity, such as studying a mental disorder. The main problem is that real-world networks present natural fluctuations, which we should consider. In this context, spectral density is an exciting feature because graphs generated by different models present distinct spectral densities, thus presenting different connectivity structures. We introduce two clustering methods: k-means for graphs of the same size and gCEM, a model-based approach for graphs of different sizes. We evaluated their performance in toy models. Finally, we applied them to FBNs of monkeys under anesthesia and a dataset of chemical compounds. We show that our methods work well in both toy models and real-world data. They present good results for clustering graphs presenting different connectivity structures even when they present the same number of edges, vertices, and degree of centrality. We recommend using k-means-based clustering for graphs when graphs present the same number of vertices and the gCEM method when graphs present a different number of vertices.

Effects of optogenetic locus coeurleus stimulation on functional connectivity in a rat model of Alzheimer’s disease

AbstractBackgroundActivity of the locus coeruleus (LC) influences behaviors that go awry in prodromal Alzheimer’s disease (AD), and these abnormalities coincide with early accumulation of hyperphosphorylated tau in the LC. LC stimulation changes global functional connectivity, but it is unknown how AD‐like neuropathology affects the LC’s ability to regulate brain state. Therefore, we combined optogenetics and functional magnetic resonance imaging (fMRI) in the TgF344‐AD rats that develop endogenous hyperphosphorylated tau in the LC prior to amyloid or tau pathology in forebrain regions.MethodsAt 2 months of age, TgF344‐AD rats and wild‐type (WT) littermates were infused intra‐LC with a ChR2‐mCherry or an mCherry virus under the noradrenergic‐specific PRSx8 promoter (n=3/group). 2‐4 weeks prior to fMRI, rats were implanted with an optic fiber targeting the LC, and functional stimulation was confirmed using pupillometry. At approximately 6 months, rats underwent fMRI scans with and without 2Hz optogenetic stimulation. During scanning, rats were intubated and subcutaneously given 1.5mg/kg*hr pancuronium while anesthetized with 1.3% isoflurane. fMRI scans were gradient echo EPI (TR=1.25s) and obtained using a 9.4T Bruker Animal MRI scanner. Data were preprocessed using a custom Rodent Whole‐Brain fMRI Data Preprocessing Toolbox. Functional connectivity matrices were generated in MATLAB using Pearson’s linear correlations between parcellated regions of interest (ROI). Histograms were generated in MATLAB by collecting the fisher z‐transformed correlation coefficients across all ROIs for the averages of each group.ResultsPupillometry confirmed optogenetic activation of the LC in ChR2 animals but not mCherry controls. Similar functional connectivity was observed between Tg and WT animals. We next subtracted baseline fisher z‐transformed correlation coefficients from those during 2Hz stimulation within each genotype to determine whether LC stimulation shifted functional connectivity. Across all groups, effects of 2Hz LC stimulation were negligible.ConclusionsTg and WT rats had similar resting‐state functional connectivity at 6 months, and 2Hz LC optogenetic stimulation did not impact functional connectivity. However, functional connectivity deficits progress with age in this strain, and the 2Hz stimulation falls within normal LC pacemaker firing (∼0.5‐2Hz). Therefore, future work will incorporate different stimulation frequency, patterns, and older rats.

Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study.

Dysfunctional brain networks have been found in patients with major depressive disorder (MDD). In this study, to verify this in a more straightforward way, we investigated the intrinsic organization of brain networks in MDD by leveraging the resting-state functional near-infrared spectroscopy (rs-fNIRS). Thirty-four MDD patients (24 females, 38.41 ± 13.14 years old) and thirty healthy controls (22 females, 34.43 ± 5.03 years old) underwent a 10 min rest while their brain activity was recorded via fNIRS. The results showed that MDD patients and healthy controls exhibited similar resting-state functional connectivity. Moreover, the depression group showed lower small-world Lambda (1.12 ± 0.04 vs. 1.16 ± 0.10, p = 0.04) but higher global efficiency (0.51 ± 0.03 vs. 0.48 ± 0.05, p = 0.03) than the control group. Importantly, MDD patients, as opposed to healthy controls, showed a significantly lower nodal local efficiency at the left middle occipital gyrus (0.56 ± 0.36 vs. 0.81 ± 0.20, pFDR &lt; 0.05), which predicted the level of depression in MDD (r = 0.45, p = 0.01, R2 = 0.15). In sum, we found a more integrated brain network in MDD patients with a lower nodal local efficiency at the occipital hub, which could predict depressive symptoms.

Frontoparietal connectivity as a product of convergent evolution in rodents and primates: functional connectivity topologies in grey squirrels, rats, and marmosets

Robust frontoparietal connectivity is a defining feature of primate cortical organization. Whether mammals outside the primate order, such as rodents, possess similar frontoparietal functional connectivity organization is a controversial topic. Previous work has primarily focused on comparing mice and rats to primates. However, as these rodents are nocturnal and terrestrial, they rely much less on visual input than primates. Here, we investigated the functional cortical organization of grey squirrels which are diurnal and arboreal, thereby better resembling primate ecology. We used ultra-high field resting-state fMRI data to compute and compare the functional connectivity patterns of frontal regions in grey squirrels (Sciurus carolinensis), rats (Rattus norvegicus), and marmosets (Callithrix jacchus). We utilized a fingerprinting analysis to compare interareal patterns of functional connectivity from seeds across frontal cortex in all three species. The results show that grey squirrels, but not rats, possess a frontoparietal connectivity organization that resembles the connectivity pattern of marmoset lateral prefrontal cortical areas. Since grey squirrels and marmosets have acquired an arboreal way of life but show no common arboreal ancestor, the expansion of the visual system and the formation of a frontoparietal connectivity architecture might reflect convergent evolution driven by similar ecological niches in primates and tree squirrels.

Frontal cortical functional connectivity is impacted by anaesthesia in macaques.

A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional magnetic resonance imaging (rs-fMRI) in anaesthetized nonhuman primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity organization of a well-characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques show that rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas, while more caudal seeds displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrated a similar functional connectivity (FC) organization of the cingulo-frontal cortical network in awake macaque to that previously uncovered in the human brain pointing toward a preserved FC organization from macaque to human. However, it can only be observed in awake state suggesting that this network is sensitive to anaesthesia and warranting significant caution when comparing FC patterns across species under different states.

The influence of EPI parameter choice on reliability of sgACC-DLPFC functional connectivity

Abstract Background: Clinical treatment efficacy of TMS targets on left DLPFC has been related to functional connectivity to the sgACC (Fox et al., 2012; Salomons et al., 2014; Cash et al., 2020). Resting-state fMRI (rs-fMRI) is increasingly used to derive these functional TMS targets in clinical settings (Weigand, 2018) Therefore, the reliability of the acquired functional targets must be quantified and assessed. Here we assessed the influence of echo time (TE) parameter choice on the reliability of the sgACC-DLPFC target network. Methods: Three runs of rs-fMRI with short TE (30ms) and three runs with long TE (38ms) were acquired on a 3T Prisma Fit scanner in five healthy subjects. We counterbalanced the order of the two protocols within runs and between participants. Functional connectivity maps were calculated for each run and seed voxel correlations were computed with a sgACC seed. Intraclass Correlation Coefficients (ICC) were calculated for each run. Values below 0.5, between 0.5 and 0.75, 0.75 and 0.90, and greater than 0.90 are considered to indicate poor, moderate, good and excellent reliability (Koo and Li, 2016). Results: Overall, similar functional connectivity networks were derived for both TE choices. However, connectivity maps acquired with TE = 38ms showed higher ICC values within the DLPFC region. Specifically, the peak within the left DLPFC had a reliability value of 0.95 for TE = 38ms compared to 0.54 for TE = 30ms. The figure below shows the ICC reliability maps for both TEs. Conclusions: Resting-state acquisition echo time affects the reliability of the resulting functional connectivity maps. This has an effect on TMS targeting approaches that base their DLPFC target on its functional connectivity to the sgACC. Our results clearly indicate that optimized acquisition parameters are beneficial for ensuring high-reliability resting-state acquisition protocols to be used for TMS targeting. Keywords: circuit-target, reliability, resting-state connectivity, sgACC-DLPFC

Measuring Glycolytic Activity with Hyperpolarized [2H7, U-13C6] D-Glucose in the Naive Mouse Brain under Different Anesthetic Conditions.

Glucose is the primary fuel for the brain; its metabolism is linked with cerebral function. Different magnetic resonance spectroscopy (MRS) techniques are available to assess glucose metabolism, providing complementary information. Our first aim was to investigate the difference between hyperpolarized 13C-glucose MRS and non-hyperpolarized 2H-glucose MRS to interrogate cerebral glycolysis. Isoflurane anesthesia is commonly employed in preclinical MRS, but it affects cerebral hemodynamics and functional connectivity. A combination of low doses of isoflurane and medetomidine is routinely used in rodent functional magnetic resonance imaging (fMRI) and shows similar functional connectivity, as in awake animals. As glucose metabolism is tightly linked to neuronal activity, our second aim was to assess the impact of these two anesthetic conditions on the cerebral metabolism of glucose. Brain metabolism of hyperpolarized 13C-glucose and non-hyperpolaized 2H-glucose was monitored in two groups of mice in a 9.4 T MRI system. We found that the very different duration and temporal resolution of the two techniques enable highlighting the different aspects in glucose metabolism. We demonstrate (by numerical simulations) that hyperpolarized 13C-glucose reports on de novo lactate synthesis and is sensitive to cerebral metabolic rate of glucose (CMRGlc). We show that variations in cerebral glucose metabolism, under different anesthesia, are reflected differently in hyperpolarized and non-hyperpolarized X-nuclei glucose MRS.

Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

Comparative connectivity correlates of dystonic and essential tremor deep brain stimulation

The pathophysiology of dystonic tremor and essential tremor remains partially understood. In patients with medication-refractory dystonic tremor or essential tremor, deep brain stimulation (DBS) targeting the thalamus or posterior subthalamic area has evolved into a promising treatment option. However, the optimal DBS targets for these disorders remains unknown. This retrospective study explored the optimal targets for DBS in essential tremor and dystonic tremor using a combination of volumes of tissue activated estimation and functional and structural connectivity analyses. We included 20 patients with dystonic tremor who underwent unilateral thalamic DBS, along with a matched cohort of 20 patients with essential tremor DBS. Tremor severity was assessed preoperatively and approximately 6 months after DBS implantation using the Fahn-Tolosa-Marin Tremor Rating Scale. The tremor-suppressing effects of DBS were estimated using the percentage improvement in the unilateral tremor-rating scale score contralateral to the side of implantation. The optimal stimulation region, based on the cluster centre of gravity for peak contralateral motor score improvement, for essential tremor was located in the ventral intermediate nucleus region and for dystonic tremor in the ventralis oralis posterior nucleus region along the ventral intermediate nucleus/ventralis oralis posterior nucleus border (4 mm anterior and 3 mm superior to that for essential tremor). Both disorders showed similar functional connectivity patterns: a positive correlation between tremor improvement and involvement of the primary sensorimotor, secondary motor and associative prefrontal regions. Tremor improvement, however, was tightly correlated with the primary sensorimotor regions in essential tremor, whereas in dystonic tremor, the correlation was tighter with the premotor and prefrontal regions. The dentato-rubro-thalamic tract, comprising the decussating and non-decussating fibres, significantly correlated with tremor improvement in both dystonic and essential tremor. In contrast, the pallidothalamic tracts, which primarily project to the ventralis oralis posterior nucleus region, significantly correlated with tremor improvement only in dystonic tremor. Our findings support the hypothesis that the pathophysiology underpinning dystonic tremor involves both the cerebello-thalamo-cortical network and the basal ganglia-thalamo-cortical network. Further our data suggest that the pathophysiology of essential tremor is primarily attributable to the abnormalities within the cerebello-thalamo-cortical network. We conclude that the ventral intermediate nucleus/ventralis oralis posterior nucleus border and ventral intermediate nucleus region may be a reasonable DBS target for patients with medication-refractory dystonic tremor and essential tremor, respectively. Uncovering the pathophysiology of these disorders may in the future aid in further improving DBS outcomes.

Innate connectivity patterns drive the development of the visual word form area

What determines the functional organization of cortex? One hypothesis is that innate connectivity patterns, either structural or functional connectivity, set up a scaffold upon which functional specialization can later take place. We tested this hypothesis by asking whether the visual word form area (VWFA), an experience-driven region, was already functionally connected to proto language networks in neonates scanned within one week of birth. Using the data from the Human Connectone Project (HCP) and the Developing Human Connectome Project (dHCP), we calculated intrinsic functional connectivity during resting-state functional magnetic resonance imaging (fMRI), and found that neonates showed similar functional connectivity patterns to adults. We observed that (1) language regions connected more strongly with the putative VWFA than other adjacent ventral visual regions that also show foveal bias, and (2) the VWFA connected more strongly with frontotemporal language regions than with regions adjacent to these language regions. These data suggest that the location of the VWFA is earmarked at birth due to its connectivity with the language network, providing evidence that innate connectivity instructs the later refinement of cortex.

The Subgrouping Structure of Newborns with Heterogenous Brain-Behavior Relationships.

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain-behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain-behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain-behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain-behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain-behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain-behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Similarity of individual functional brain connectivity patterns formed by music listening quantified with a data-driven approach.

This study aims to explore the similarities in functional connectivity (FC) patterns in individuals when listening to different music genres and, in comparison, to the spoken word, using a novel data-driven approach. Our model and findings can potentially be utilized for evaluating the neurological effects of therapeutic music interventions. Twelve healthy volunteers listened to seven different sound tracks while undergoing functional magnetic resonance imaging (fMRI) scans: music of the volunteer's choice with positive emotional attachment, two selections of unfamiliar classical music, one classical piece repeated with visual guidance and three spoken language tracks. FC network graphs were created, and selected graph properties were evaluated toward their commonalities across sound tracks. For comparison, FC patterns represented by the graph adjacency matrices were directly compared for high and low BOLD activation during listening. Graph properties averaged across subjects showed similar values for the same sound track compared to different sound tracks (p < 0.003). For high BOLD activation involving most areas in the auditory cortex, FC patterns for the same sound track correlated highly (0.74 ± 0.11), whereas FC patterns for different sound tracks did not (0.09 ± 0.07; p < 6e-5). For low BOLD activation involving additional brain regions, correlation of FC patterns for the sound tracks was still higher (0.43 ± 0.07) than for different sound tracks (0.09 ± 0.05; p < 8e-6). Similar music creates similar functional activation and connectivity patterns in the brain of healthy individuals as does listening to the spoken word. Direct comparison of FC patterns yielded higher correlations than indirect comparisons of graph properties derived from corresponding FC networks.

Resting-State fMRI Study of ADHD and Internet Gaming Disorder

Objective: We aimed to understand whether Attention Deficit Hyperactivity Disorder (ADHD) and Internet gaming disorder (IGD) share similar brain functional connectivity (FC) between the frontal and subcortices. Method: We compared changes in clinical symptoms and brain activity using functional magnetic resonance imaging (fMRI) in 26 patients with ADHD but without IGD, 29 patients with ADHD and IGD, and 20 patients with IGD but without ADHD. Results: The functional connectivity (FC) from the cortex to subcortex in both groups was decreased relative to that in age-matched healthy participants. One-year treatment for ADHD and IGD symptoms increased the FC between the cortex and subcortex in all ADHD participants and all IGD participants with good prognoses compared with those in all ADHD participants and all IGD participants with poor prognoses. Conclusion: Patients with ADHD and IGD shared similar brain FC at baseline and FC changes in response to treatment.

What is the Functional Difference Between Sagittal With Metopic and Isolated Sagittal Craniosynotosis?

The purpose of this study is to understand the neurological differences between patients born with combined sagittal and metopic craniosynostosis (SMc) and isolated sagittal craniosynostosis (ISc) by studying aberrations in functional brain connectivity and white matter microstructure, before surgery, utilizing functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The authors collected DTI and resting-state (ie, no sedation and asleep) functional connectivity MRI data in 10 infant patients preoperatively: 5 in the SMc group (4.3 ± 1 months) and 5 in the ISc group (4.8 ± 1.1 months). Resting state fMRI imaging and DTI data were acquired using a 3-T Siemens Trio MRI system (Erlangen, Germany) while the infant patients slept. fMRI data were corrected for movement using SPM, underwent cerebrospinal fluid and white matter signal regression and further analyzed with BioImageSuite. For the DTI data, 3 diffusion runs were averaged, processed utilizing FMRIB Software Library, and analyzed statistically using BioImageSuite. Comparing the SMc versus ISc groups, SMc demonstrated that there was increased connectivity, statistically significant differences, in neural networks between children with sagittal synostosis alone versus those with sagittal with metopic synostosis, in the right BA 31 and BA 23 (corresponding to the posterior cingulate cortex (PCC) (P < 0.001). Analysis of the DTI revealed increased fractional anisotropy (normal maturation of white tracts) in the SMc group in the cingulum compared to the ISc group (P < 0.05). Differences in the functional networks include increased connectivity right frontoparietal network (RFPN) in ISc and increased connectivity in the primary visual network (V1) in SMc (P < 0.001). The SMc had increased connectivity as measured by fMR in the PCC, an area associated with attention deficit hyperactivity disorder. The DTI analysis demonstrated an increase in fractional anisotropy of the cingulum in the SMc group, a white matter tract projecting from the cingulate cortex; connections of the limbic (emotional regulation) system are instrumental. In SMc, increase of connectivity in the PCC correlates with an increase in maturation of the cingulum compared to ISc. There is increased connectivity of the RFPN network in the ISc and increased connectivity of the V1 network in the SMc patients. The SMc group has increased connectivity in the PCC, the original seed of the DMN network, and decreased connectivity to the RFPN network. The pattern of increased connectivity in the area of the DMN and decreased connectivity in the RFPN network is similar to the trend when comparing ADHD patients to normal controls. SMc has more similar functional network connectivity to ADHD as compared to ISc.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities.

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks

Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric.To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.

Altered Functional Connectivity Observed at Rest in Children and Adolescents Prenatally Exposed to Alcohol.

Studies of brain structure in fetal alcohol spectrum disorder (FASD) have shown the global and focal effects that prenatal alcohol exposure (PAE) has on the brain, suggesting but not measuring altered function in FASD. This study aimed to (1) identify resting-state functional networks in children and adolescents with FASD, (2) investigate functional connectivity differences compared with healthy controls, and (3) assess the links to cognitive deficits. Participants included 66 children/adolescents with FASD (aged 5.5-18.9 years) and 67 healthy controls (aged 5.8-18.5 years) scanned across four sites as part of the NeuroDevNet study. Six core functional networks with 27 regions of interest (ROIs) were examined using seed-based and ROI-to-ROI analyses. Average seed-based connectivity maps showed significant spatial overlap of positively correlated regions for all six core networks between FASD and controls, but there was less overlap for negatively correlated regions. ROI-to-ROI matrices demonstrated lower internetwork connectivity between regions primarily associated with the salience network (anterior cingulate cortex and bilateral insula), frontal-parietal network (bilateral posterior parietal cortex), and language network (right posterior superior temporal gyrus). Post hoc correlations of the FASD participants without medication revealed a relationship between functional connectivity and performance on two cognitive tests associated with mathematics ability and attention. Even though participants with PAE exhibit very similar intranetwork functional connectivity patterns as controls, their lower internetwork functional connectivity suggests underlying deficits in the functional network brain architecture that may be related to cognitive impairment.

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.

The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.