Functional segmentation of the brain cortex using high model order group PICA

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Altered dynamic functional and effective connectivity in drug-naive children with Tourette syndrome

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by repetitive, stereotyped, involuntary tics, the neurological basis of which remains unclear. Although traditional resting-state MRI (rfMRI) studies have identified abnormal static functional connectivity (FC) in patients with TS, dynamic FC (dFC) remains relatively unexplored. The rfMRI data of 54 children with TS and 46 typically developing children (TDC) were analyzed using group independent component analysis to obtain independent components (ICs), and a sliding-window approach to generate dFC matrices. All dFC matrices were clustered into two reoccurring states, the state transition metrics were obtained. We conducted Granger causality and nodal topological analyses to further investigate the brain regions that may play the most important roles in driving whole-brain switching between different states. We found that children with TS spent more time in state 2 (PFDR < 0.001), a state characterized by strong connectivity between ICs, and switched more quickly between states (PFDR = 0.025) than TDC. The default mode network (DMN) may play an important role in abnormal state transitions because the FC that changed the most between the two states was between the DMN and other networks. Additionally, the DMN had increased degree centrality, efficiency and altered causal influence on other networks. Certain alterations related to executive function (r = -0.309, P < 0.05) and tic symptom ratings (r = 0.282; 0.413, P < 0.05) may represent important aspects of the pathophysiology of TS. These findings facilitate our understanding of the neural basis for the clinical presentation of TS.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study

OBJECTIVE

Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine.

METHODS

We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results.

RESULTS

The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86.

INTERPRETATION

Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

Large-scale brain functional network abnormalities in social anxiety disorder

BACKGROUND

Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value.

METHODS

Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses.

RESULTS

SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy.

CONCLUSIONS

SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

Introduction

Functional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis.

Materials and methods

In this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities.

Results

Our data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 % similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data.

Conclusion

Our analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.

Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls

Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.

Altered temporal connectivity and reduced meta-state dynamism in adolescents born very preterm

Adolescents born very preterm have an increased risk for anxiety, social difficulties and inattentiveness, i.e. the 'preterm behavioural phenotype'. The extreme end of these traits comprises the core diagnostic features of attention and hyperactivity disorders and autism spectrum disorder, which have been reported to show aberrant dynamic resting-state functional network connectivity. This study aimed to compare this dynamism between adolescents born very preterm and controls. A resting-state functional magnetic resonance imaging was performed on 24 adolescents born very preterm (gestational age <32 weeks and/or birth weight ≤1500 g) and 32 controls born full term (≥37 weeks of gestation) at 13 years of age. Group-wise comparisons of dynamic connectivity between the resting-state networks were performed using both hard clustering and meta-state analysis of functional network connectivity. The very preterm group yielded a higher fraction of time spent in the least active connectivity state in hard clustering state functional network connectivity, even though no group differences in pairwise connectivity patterns were discovered. The meta-state analysis showed a decreased fluidity and dynamic range in the very preterm group compared with controls. Our results suggest that the 13-year-old adolescents born very preterm differ from controls in the temporal characteristics of functional connectivity. The findings may reflect the long-lasting effects of prematurity and the clinically acknowledged 'preterm behavioural phenotype'.

Social reappraisal of emotions is linked with the social presence effect in the default mode network

Introduction

Social reappraisal, during which one person deliberately tries to regulate another's emotions, is a powerful cognitive form of social emotion regulation, crucial for both daily life and psychotherapy. The neural underpinnings of social reappraisal include activity in the default mode network (DMN), but it is unclear how social processes influence the DMN and thereby social reappraisal functioning. We tested whether the mere presence of a supportive social regulator had an effect on the DMN during rest, and whether this effect in the DMN was linked with social reappraisal-related neural activations and effectiveness during negative emotions.

Methods

A two-part fMRI experiment was performed, with a psychotherapist as the social regulator, involving two resting state (social, non-social) and two task-related (social reappraisal, social no-reappraisal) conditions.

Results

The psychotherapist's presence enhanced intrinsic functional connectivity of the dorsal anterior cingulate (dACC) within the anterior medial DMN, with the effect positively related to participants' trust in psychotherapists. Secondly, the social presence-induced change in the dACC was related with (a) the social reappraisal-related activation in the bilateral dorsomedial/dorsolateral prefrontal cortex and the right temporoparietal junction and (b) social reappraisal success, with the latter relationship moderated by trust in psychotherapists.

Conclusion

Results demonstrate that a psychotherapist's supportive presence can change anterior medial DMN's intrinsic connectivity even in the absence of stimuli and that this DMN change during rest is linked with social reappraisal functioning during negative emotions. Data suggest that trust-dependent social presence effects on DMN states are relevant for social reappraisal-an idea important for daily-life and psychotherapy-related emotion regulation.

Aberrant resting-state functional connectivity associated with childhood trauma among juvenile offenders

Individuals with history of childhood trauma are characterized by aberrant resting-state limbic and paralimbic functional network connectivity. However, it is unclear whether specific subtypes of trauma (i.e., experienced vs observed or community) showcase differential effects. This study examined whether subtypes of childhood trauma (assessed via the Trauma Checklist [TCL] 2.0) were associated with aberrant intra-network amplitude of fluctuations and connectivity (i.e., functional coherence within a network), and inter-network connectivity across resting-state networks among incarcerated juvenile males (n = 179). Subtypes of trauma were established via principal component analysis of the TCL 2.0 and resting-state networks were identified by applying group independent component analysis to resting-state fMRI scans. We tested the association of subtypes of childhood trauma (i.e., TCL Factor 1 measuring experienced trauma and TCL Factor 2 assessing community trauma), and TCL Total scores to the aforementioned functional connectivity measures. TCL Factor 2 scores were associated with increased high-frequency fluctuations and increased intra-network connectivity in cognitive control, auditory, and sensorimotor networks, occurring primarily in paralimbic regions. TCL Total scores exhibited similar neurobiological patterns to TCL Factor 2 scores (with the addition of aberrant intra-network connectivity in visual networks), and no significant associations were found for TCL Factor 1. Consistent with previous analyses of community samples, our results suggest that childhood trauma among incarcerated juvenile males is associated with aberrant intra-network amplitude of fluctuations and connectivity across multiple networks including predominately paralimbic regions. Our results highlight the importance of accounting for traumatic loss, observed trauma, and community trauma in assessing neurobiological aberrances associated with adverse experiences in childhood, as well as the value of trained-rater trauma assessments compared to self-report.

Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits

Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.

Association of Visual Health With Depressive Symptoms and Brain Imaging Phenotypes Among Middle-Aged and Older Adults

IMPORTANCE

Vision loss and depression are common conditions with major health implications. However, mechanisms of the association of visual health (across the full acuity spectrum) with depression remain unclear.

OBJECTIVE

To characterize the association between visual health and depression and investigate the association between depression and brain microstructure and macrostructure in subgroups divided by visual acuity.

DESIGN, SETTING, AND PARTICIPANTS

In the UK Biobank Study cohort, 114 583 volunteers were included at baseline from March to June 2006 to July 2010. Habitual distance visual acuity was examined using the logarithm of the minimum angle of resolution (LogMAR) characters. Depression was identified based on Patient Health Questionnaire (PHQ) or through an interview-based psychiatric diagnosis. Subgroup participants completed multimodal magnetic resonance imaging (MRI) of the brain and PHQ evaluation during the imaging visit after 2014. Data were analyzed from May 5 to August 9, 2022.

MAIN OUTCOMES AND MEASURES

Depression, depressive symptoms, and imaging-derived phenotypes from T1-weighted and diffusion MRI.

RESULTS

Of the 114 583 participants from the UK Biobank Study, 62 401 (54.5%) were women, and the mean (SD) age was 56.8 (8.1) years (range, 39-72 years). A 1-line worse visual acuity (0.1 LogMAR increase) was associated with 5% higher odds of depression (odds ratio, 1.05 [95% CI, 1.04-1.07]) after adjustment for age, sex, race and ethnicity, Townsend index, educational qualifications, smoking, alcohol consumption, obesity, physical activity, history of hypertension, diabetes, hyperlipidemia, and family history of depression. Of the 7844 participants eligible for MRI analysis, there were linear associations between PHQ score and the left volume of gray matter in supracalcarine cortex (coefficient, 7.61 [95% CI, 3.90-11.31]) and mean isotropic volume fraction (ISOVF) in the right fornix (cres) and/or stria terminalis (coefficient, 0.003 [95% CI, 0.001-0.004]) after correction for multiple comparison. In addition, their association could be moderated by visual acuity, whereby increased PHQ score was associated with higher ISOVF levels only among those with poorer visual acuity (P = .02 for interaction).

CONCLUSIONS AND RELEVANCE

This study suggests an association between visual health and depression and that the diffusion characteristic of ISOVF in the fornix (cres) and/or stria terminalis is associated with depressive symptoms in participants with poorer visual acuity.

Modulation of the functional connectome in major depressive disorder by ketamine therapy

BACKGROUND

Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized.

METHODS

Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status.

RESULTS

Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern.

CONCLUSION

Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake

AIMS

Currently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity.

MATERIALS AND METHODS

Twenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions.

RESULTS

Three optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain's main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants' subjective feelings after drinking.

CONCLUSION

The current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.

Using deep clustering to improve fMRI dynamic functional connectivity analysis

Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for dimensionality reduction prior to applying k-means clustering to the encoded data. We compare this deep clustering method to dimensionality reduction using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of dimensionality reduction method has a significant effect on group-level measurements of state temporal properties.

Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.

Empirical evaluation of human fetal fMRI preprocessing steps

Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.

Evaluation of functional MRI-based human brain parcellation: a review

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

Biclustering fMRI time series: a comparative study

BACKGROUND

The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain.

RESULTS

This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis.

CONCLUSIONS

This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.

Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network

The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals

Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm

High dimensionality data have become common in neuroimaging fields, especially group‐level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data‐driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE‐based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group‐level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task‐evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

The Effects of Cardiorespiratory and Motor Skill Fitness on Intrinsic Functional Connectivity of Neural Networks in Individuals with Parkinson's Disease

Background:

Studies in aging older adults have shown the positive association between cognition and exercise related fitness, particularly cardiorespiratory fitness. These reports have also demonstrated the association of high cardiorespiratory fitness, as well as other types of fitness, on the reversal of age-related decline in neural network connectivity, highlighting the potential role of fitness on age- and disease-related brain changes. While the clinical benefits of exercise are well-documented in Parkinson’s disease (PD), the extent to which cardiorespiratory fitness (assessed by estimated VO_2max testing) or motor skill fitness (assessed by the Physical Performance Test (PPT)) affects neural network connectivity in PD remains to be investigated. The purpose of this study was to explore the hypothesis that higher fitness level is associated with an increase in the intrinsic network connectivity of cognitive networks commonly affected in PD.

Methods:

In this cross-sectional resting state fMRI, we used a multivariate statistical approach based on high-dimensional independent component analysis (ICA) to investigate the association between two independent fitness metrics (estimated VO_2max and PPT) and resting state network connectivity.

Results:

We found that increased estimated VO_2max was associated with increased within network connectivity in cognitive networks known to be impaired in PD, including those sub-serving memory and executive function. There was a similar trend for high levels of PPT to be associated with increased within network connectivity in distinct resting state networks. The between functional network connectivity analysis revealed that cardiorespiratory fitness was associated with increased functional connectivity between somatosensory motor network and several cognitive networks sub-serving memory, attention, and executive function.

Conclusion:

This study provides important empirical data supporting the potential association between two forms of fitness and multiple resting state networks impacting PD cognition. Linking fitness to circuit specific modulation of resting state network connectivity will help establish a neural basis for the positive effects of fitness and specific exercise modalities and provide a foundation to identify underlying mechanisms to promote repair.

Abnormal Brain Functional Network Dynamics in Acute CO Poisoning

Aims: Carbon monoxide poisoning is a common condition that can cause severe neurological sequelae. Previous studies have revealed that functional connectivity in carbon monoxide poisoning is abnormal under the assumption that it is resting during scanning and have focused on studying delayed encephalopathy in carbon monoxide poisoning. However, studies of functional connectivity dynamics in the acute phase of carbon monoxide poisoning may provide a more insightful perspective for understanding the neural mechanisms underlying carbon monoxide poisoning. To our knowledge, this is the first study that explores abnormal brain network dynamics in the acute phase of carbon monoxide poisoning.

Methods: Combining the sliding window method and k-means algorithm, we identified four recurrent dynamic functional cognitive impairment states from resting-state functional magnetic resonance imaging data from 29 patients in the acute phase of carbon monoxide poisoning and 29 healthy controls. We calculated between-group differences in the temporal properties and intensity of dFC states, and we also performed subgroup analyses to separately explore the brain network dynamics characteristics of adult vs. child carbon monoxide poisoning groups. Finally, these differences were correlated with patients’ cognitive performance in the acute phase of carbon monoxide poisoning and coma duration.

Results: We identified four morphological patterns of brain functional network connectivity. During the acute phase of carbon monoxide poisoning, patients spent more time in State 2, which is characterized by positive correlation between SMN and CEN, and negative correlation between DMN and SMN. In addition, the fractional window and mean dwell time of State 2 were positively correlated with coma duration. The subgroup analysis results demonstrated that the acute phase of childhood carbon monoxide poisoning had greater dFNC time variability than adult carbon monoxide poisoning.

Conclusion: Our findings reveal that patients in the acute phase of carbon monoxide poisoning exhibit dynamic functional abnormalities. Furthermore, children have greater dFNC instability following carbon monoxide poisoning than adults. This advances our understanding of the pathophysiological mechanisms underlying acute carbon monoxide poisoning.

Mapping thalamocortical functional connectivity with large-scale brain networks in patients with first-episode psychosis

Abnormal thalamocortical networks involving specific thalamic nuclei have been implicated in schizophrenia pathophysiology. While comparable topography of anatomical and functional connectivity abnormalities has been reported in patients across illness stages, previous functional studies have been confined to anatomical pathways of thalamocortical networks. To address this issue, we incorporated large-scale brain network dynamics into examining thalamocortical functional connectivity. Forty patients with first-episode psychosis and forty healthy controls underwent T1-weighted and resting-state functional magnetic resonance imaging. Independent component analysis of voxelwise thalamic functional connectivity maps parcellated the cortex into thalamus-related networks, and thalamic subdivisions associated with these networks were delineated. Functional connectivity of (1) networks with the thalamus and (2) thalamic subdivision seeds were examined. In patients, functional connectivity of the salience network with the thalamus was decreased and localized to the ventrolateral (VL) and ventroposterior (VP) thalamus, while that of a network comprising the cerebellum, temporal and parietal regions was increased and localized to the mediodorsal (MD) thalamus. In patients, thalamic subdivision encompassing the VL and VP thalamus demonstrated hypoconnectivity and that encompassing the MD and pulvinar regions demonstrated hyperconnectivity. Our results extend the implications of disrupted thalamocortical networks involving specific thalamic nuclei to dysfunctional large-scale brain network dynamics in schizophrenia pathophysiology.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

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We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data.

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sPROFUMO hierarchically estimates fMRI networks for the population and every individual.

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We characterised high dimensional resting state fMRI networks from UK Biobank.

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Model outperforms ICA and dual regression for estimation of individual-specific network topography.

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We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

The Cerebellum Is a Common Key for Visuospatial Execution and Attention in Parkinson's Disease

Cognitive decline affects the clinical course in patients with Parkinson's disease (PD) and contributes to a poor prognosis. However, little is known about the underlying network-level abnormalities associated with each cognitive domain. We aimed to identify the networks related to each cognitive domain in PD using resting-state functional magnetic resonance imaging (MRI). Forty patients with PD and 15 normal controls were enrolled. All subjects underwent MRI and the Mini-Mental State Examination. Furthermore, the cognitive function of patients with PD was assessed using the Montreal Cognitive Assessment (MoCA). We used independent component analysis of the resting-state functional MRI for functional segmentation, followed by reconstruction to identify each domain-related network, to predict scores in PD using multiple regression models. Six networks were identified, as follows: the visuospatial-executive-domain-related network (R² = 0.54, p < 0.001), naming-domain-related network (R² = 0.39, p < 0.001), attention-domain-related network (R² = 0.86, p < 0.001), language-domain-related network (R² = 0.64, p < 0.001), abstraction-related network (R² = 0.10, p < 0.05), and orientation-domain-related network (R² = 0.64, p < 0.001). Cerebellar lobule VII was involved in the visuospatial-executive-domain-related and attention-domain-related networks. These two domains are involved in the first three listed nonamnestic cognitive impairment in the diagnostic criteria for PD with dementia (PDD). Furthermore, Brodmann area 10 contributed most frequently to each domain-related network. Collectively, these findings suggest that cerebellar lobule VII may play a key role in cognitive impairment in nonamnestic types of PDD.

Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks

Connectivity within the human connectome occurs between multiple neuronal systems-at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following: (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps. Artifactual overfitting occurred to a minor degree at high dimensionalities. Basic summary statistics on spatial maps (maximum cluster size, maximum component weight, and average weight outside of maximum cluster) quickly and easily separated artifacts from gray matter sources. Lastly, by using weighted averages of bootstrap stability, even ultra-high dimensional ICA resulted in highly reproducible spatial maps. These results demonstrate how ICA can be applied in multi-scale analyses, reliably and accurately reproducing the hierarchy of meta-networks, large-scale networks, and subnetworks, thereby characterizing cortical connectivity across multiple spatial scales.

Extracting representations of cognition across neuroimaging studies improves brain decoding

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.

Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA

Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the "DMN-splitting" in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.

Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.

Combining network topology and information theory to construct representative brain networks

Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.

Altered brain functional network dynamics in obsessive-compulsive disorder

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

Multimodal Risk-Based Path Planning for Neurosurgical Interventions

Image-guided neurosurgical interventions are challenging due to the complex anatomy of the brain and the inherent risk of damaging vital structures. This paper presents a neurosurgical planning tool for safe and effective neurosurgical interventions, minimizing the risk through optimized access planning. The strengths of the proposed system are the integration of multiple risk structures combined into a holistic model for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons to quickly determine the most appropriate surgical entry point and trajectory through the brain with minimized risk. The user interface guides a user through the decision-making process and may save planning time of neurosurgical interventions. The navigation tool has been interfaced to the Robot Operating System, which allows the integration into automated workflows and the planning of linear and nonlinear trajectories. Determined risk structures and trajectories can be visualized intuitively as a projection map on the skin or cortical surface. Two risk calculation modes (strict and joint) are offered to the neurosurgeons, depending on the intracranial procedure's type and complexity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system.

Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks

Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.

Consistency of independent component analysis for FMRI

BACKGROUND

Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs).

NEW METHOD

In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, "consistent components" (CCs) are defined as those which can be extracted repeatably over a range of model orders.

RESULT

The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise.

COMPARISON WITH EXISTING METHODS

The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method.

CONCLUSIONS

This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.

Ultra-high field (10.5 T) resting state fMRI in the macaque

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with altered patterns of functional brain connectivity. Little is currently known about how moment-to-moment brain dynamics differ in children with ASD and typically developing (TD) children. Altered functional integrity of the midcingulo-insular network (M-CIN) has been implicated in the neurobiology of ASD. Here we use a novel coactivation analysis approach applied to a large sample of resting-state fMRI data collected from children with ASD and TD children to demonstrate altered patterns of M-CIN dynamics in children with the disorder. We speculate that these atypical patterns of brain dynamics may underlie behavioral inflexibility in ASD.

Modelling subject variability in the spatial and temporal characteristics of functional modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment

Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer's disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed inter-network high-order FC (IN-HOFC), as a useful complement to the traditional inter-network FC methods, for characterizing more complex organizations among the large-scale brain networks. In the IN-HOFC, both network definition and inter-network FC are defined in a high-order manner. To test whether IN-HOFC is more sensitive to cognition decline due to brain diseases than traditional inter-network FC, 77 mild cognitive impairments (MCIs) and 89 controls are compared among the conventional methods and our IN-HOFC. The result shows that IN-HOFCs among three temporal lobe-related high-order networks are dampened in MCIs. The impairment of IN-HOFC is especially found between the anterior and posterior medial temporal lobe and could be a potential MCI biomarker at the network level. The competing network-level low-order FC methods, however, either revealing less or failing to detect any group difference. This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies.

Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia

The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms.

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording

Pupillometry, a noninvasive measure of arousal, complements human functional MRI (fMRI) to detect periods of variable cognitive processing and identify networks that relate to particular attentional states. Even under anesthesia, pupil dynamics correlate with brain-state fluctuations, and extended dilations mark the transition to more arousable states. However, cross-scale neuronal activation patterns are seldom linked to brain state-dependent pupil dynamics. Here, we complemented resting-state fMRI in rats with cortical calcium recording (GCaMP-mediated) and pupillometry to tackle the linkage between brain-state changes and neural dynamics across different scales. This multimodal platform allowed us to identify a global brain network that covaried with pupil size, which served to generate an index indicative of the brain-state fluctuation during anesthesia. Besides, a specific correlation pattern was detected in the brainstem, at a location consistent with noradrenergic cell group 5 (A5), which appeared to be dependent on the coupling between different frequencies of cortical activity, possibly further indicating particular brain-state dynamics. The multimodal fMRI combining concurrent calcium recordings and pupillometry enables tracking brain state-dependent pupil dynamics and identifying unique cross-scale neuronal dynamic patterns under anesthesia.

Aberrant Executive Control and Auditory Networks in Recent-Onset Schizophrenia

PURPOSE

Despite a large number of resting-state functional MRI (rsfMRI) studies in schizophrenia, current evidence on the abnormalities of functional connectivity (FC) of resting-state networks shows high variability, and the findings on recent-onset schizophrenia are insufficient compared to those on chronic schizophrenia.

PATIENTS AND METHODS

We performed a rsfMRI in 46 patients with recent-onset schizophrenia and 22 healthy controls. Group independent component brainmap and dual regression were performed for voxel-wise comparisons between the groups. Correlation of the symptom severity, cognitive function, duration of illness, and a total antipsychotics dose with FC was evaluated with Spearman's rho correlation.

RESULTS

The patient group had areas with a significantly decreased FC compared to that of the control group in which it existed in the left supplementary motor cortex and supramarginal gyrus (the executive control network) and the right postcentral gyrus (the auditory network). The patient group had a significant correlation of the total antipsychotics dose with the FC of the cluster in the left supplementary motor cortex in the executive control network.

CONCLUSION

Patients with recent-onset schizophrenia have decreased FC of the executive control and auditory networks compared to healthy controls.