Resting-state fMRI: a review of methods and clinical applications

Categorizing cortical dysplasia lesions for surgical outcome using network functional connectivity

OBJECTIVE

Focal cortical dysplasia (FCD) is often associated with drug-resistant epilepsy, leading to a recommendation to surgically remove the seizure focus. Predicting outcome for resection of FCD is challenging, requiring a new approach. Lesion-symptom mapping is a powerful and broadly applicable method for linking neurological symptoms or outcomes to damage to particular brain regions. In this work, the authors applied lesion network mapping, an expansion of the traditional approach, to search for the association of lesion network connectivity with surgical outcomes. They hypothesized that connectivity of lesion volumes, preoperatively identified by MRI, would associate with seizure outcomes after surgery in a pediatric cohort with FCD.

METHODS

This retrospective study included 21 patients spanning the ages of 3 months to 17.7 years with FCD lesions who underwent surgery for drug-resistant epilepsy. The mean brain-wide functional connectivity map of each lesion volume was assessed across a database of resting-state functional MRI data from healthy children (spanning approximately 2.9 to 18.9 years old) compiled at the authors' institution. Lesion connectivity maps were averaged across age and sex groupings from the database and matched to each patient. The authors sought to associate voxel-wise differences in these maps with subject-specific surgical outcome (seizure free vs persistent seizures).

RESULTS

Lesion volumes with persistent seizures after surgery tended to have stronger connectivity to attention and motor networks and weaker connectivity to the default mode network compared with lesion volumes with seizure-free surgical outcome.

CONCLUSIONS

Network connectivity-based lesion-outcome mapping may offer new insight for determining the impact of lesion volumes discerned according to both size and specific location. The results of this pilot study could be validated with a larger set of data, with the ultimate goal of allowing examination of lesions in patients with FCD and predicting their surgical outcomes.

Noninvasive magnetic resonance imaging techniques in mild traumatic brain injury research and diagnosis

Mild traumatic brain injury (mTBI), frequently referred to as concussion, is one of the most common neurological disorders. The underlying neural mechanisms of functional disturbances in the brains of concussed individuals remain elusive. Novel forms of brain imaging have been developed to assess patients postconcussion, including functional magnetic resonance imaging (fMRI), susceptibility-weighted imaging (SWI), diffusion MRI (dMRI), and perfusion MRI [arterial spin labeling (ASL)], but results have been mixed with a more common utilization in the research environment and a slower integration into the clinical setting. In this review, the benefits and drawbacks of the methods are described: fMRI is an effective method in the diagnosis of concussion but it is expensive and time-consuming making it difficult for regular use in everyday practice; SWI allows detection of microhemorrhages in acute and chronic phases of concussion; dMRI is primarily used for the detection of white matter abnormalities, especially axonal injury, specific for mTBI; and ASL is an alternative to the BOLD method with its ability to track cerebral blood flow alterations. Thus, the absence of a universal diagnostic neuroimaging method suggests a need for the adoption of a multimodal approach to the neuroimaging of mTBI. Taken together, these methods, with their underlying functional and structural features, can contribute from different angles to a deeper understanding of mTBI mechanisms such that a comprehensive diagnosis of mTBI becomes feasible for the clinician.

Speech Motor Function and Auditory Perception in Succinic Semialdehyde Dehydrogenase Deficiency: Toward Pre-Supplementary Motor Area (SMA) and SMA-Proper Dysfunctions

This study reviews the fundamental roles of pre-supplementary motor area (SMA) and SMA-proper responsible for speech-motor functions and auditory perception in succinic semialdehyde dehydrogenase (SSADH) deficiency. We comprehensively searched the databases of PubMed, Google Scholar, and the electronic journals Springer, PreQuest, and Science Direct associated with keywords SSADHD, SMA, auditory perception, speech, and motor with AND operator. Transcranial magnetic stimulation emerged for assessing excitability/inhibitory M1 functions, but its role in pre-SMA and SMA proper dysfunction remains unknown. There was a lack of data on resting-state and task-based functional magnetic resonance imaging (MRI), with a focus on passive and active tasks for both speech and music, in terms of analysis of SMA-related cortex and its connections. Children with SSADH deficiency likely experience a dysfunction in connectivity between SMA portions with cortical and subcortical areas contributing to disabilities in speech-motor functions and auditory perception. Early diagnosis of auditory-motor disabilities in children with SSADH deficiency by neuroimaging techniques invites opportunities for utilizing sensory-motor integration as future interventional strategies.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Sensitivity to changes in rate of heartbeats as a measure of interoceptive ability

Individuals vary in their ability to perceive, as conscious sensations, signals like the beating of the heart. Tests of such interoceptive ability are, however, constrained in nature and reliability. Performance of the heartbeat tracking task, a widely used test of cardiac interoception, often corresponds well with individual differences in emotion and cognition, yet is susceptible to reporting bias and influenced by higher-order knowledge, e.g., of expected heart rate. The present study introduces a new way of assessing cardiac interoceptive ability, focusing on sensitivity to short-term, naturalistic changes in frequency of heartbeats. At rest, such heart rate variability typically reflects the dominant influence of respiration on vagus parasympathetic control of the sinoatrial pacemaker. We observed an overall tendency of healthy participants to report feeling fewer heartbeats during increases in heart rate, which we speculate reflects a reduction in heartbeat strength and salience during inspiratory periods when heart rate typically increases to maintain a stable cardiac output. Within-participant performance was more variable on this measure of cardiac interoceptive sensitivity relative to the "classic" heartbeat tracking task. Our findings indicate that cardiac interoceptive ability, rather than reflecting the veridical monitoring of subtle variations in physiology, appears to involve more interpolation wherein interoceptive decisions are informed by dynamic working estimates derived from the integration of afferent signaling and higher-order predictions.NEW & NOTEWORTHY This study presents a new method for evaluating cardiac interoceptive ability, measuring sensitivity to naturalistic changes in the number of heartbeats over time periods. Results show participants have an overall tendency toward sensing fewer heartbeats during higher heart rates. This likely reflects the influence of changing heartbeat strength on cardiac interoception at rest, which should be taken into account when evaluating cardiac interoceptive ability and its relationship to anxiety and psychosomatic conditions.

Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study

Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.

Altered brain activity mediates the relationship between white matter hyperintensity severity and cognition in older adults

White matter hyperintensities (WMHs) on magnetic resonance imaging are commonly found in older adults. The mechanisms underpinning the dose-dependent association between WMH severity and cognition are not well understood. This study aimed to investigate how brain activity changes with WMH severity, and if altered brain activity mediates the relationship between WMH and cognitive function. A total of 35 participants with moderate to severe WMHs (Fazekas grade 2 or 3) and 34 participants with mild WMHs (Fazekas grade 1), who were cognitively normal, were included. Resting-state brain function was analyzed using the amplitude of low-frequency fluctuation (ALFF). A mean fractional anisotropy (FA) value of 20 tract-specific regions of interest was calculated. Mediation analysis was used to assess whether ALFF values mediated the relationship between WMH and cognition. The results showed that compared to those with mild WMHs, participants with confluent WMHs had worse memory and naming ability and also had increased ALFF in the right middle frontal gyrus and decreased ALFF in the left middle occipital gyrus. After controlling for age, gender, education and apolipoprotein E (ApoE) ε4 status, increased ALFF in the right prefrontal cortex was associated with worse immediate recall and recognition, and ALFF values mediated the relationships between both Fazekas scores and FA values and memory. In conclusion, our study suggests that cognitively normal adults with high WMH load exhibit subclinical cognitive dysfunction and altered spontaneous brain activity. The mediating effects of brain activity help to shed light on our understanding of the relationship between WMHs and cognition.

Fathers' Involvement in Early Childcare is Associated with Amygdala Resting-State Connectivity

Becoming a parent requires new skills and frequent task switching during daily childcare. Little is known about the paternal brain during the transition to fatherhood. The present study examined intrinsic neuronal network connectivity in a group of first-time expectant and new fathers (total N = 131) using amygdala seed-based resting-state functional connectivity analysis. Furthermore, we examined the association between paternal involvement (i.e. hours spent in childcare and real-time push notifications on smartphone) and connectivity within the parental brain network in new fathers. There were no significant differences in functional connectivity between expectant and new fathers. However, results show that in new fathers, time spent in childcare was positively related to amygdala connectivity with the supramarginal gyrus, postcentral gyrus and the superior parietal lobule—all regions within the cognition/mentalizing network that have been associated with empathy and social cognition. Our results suggest that fathers’ time investment in childcare is related to connectivity networks in the parental brain.

Analysis of Brain Functional Connectivity Neural Circuits in Children With Autism Based on Persistent Homology

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder with a complex and unknown etiology. Statistics demonstrate that the number of people diagnosed with ASD is increasing in countries around the world. Currently, although many neuroimaging studies indicate that ASD is characterized by abnormal functional connectivity (FC) patterns within brain networks rather than local functional or structural abnormalities, the FC characteristics of ASD are still poorly understood. In this study, a Vietoris-Rips (VR) complex filtration model of the brain functional network was established by using resting-state functional magnetic resonance imaging (fMRI) data of children aged 6–13 years old [including 54 ASD patients and 52 typical development (TD) controls] from the Autism Brain Imaging Data Exchange (ABIDE) public database. VR complex filtration barcodes are calculated by using persistent homology to describe the changes in the FC neural circuits of brain networks. The number of FC neural circuits with different length ranges at different threshold values is calculated by using the barcodes, the different brain regions participating in FC neural circuits are discussed, and the connectivity characteristics of brain FC neural circuits in the two groups are compared and analyzed. Our results show that the number of FC neural circuits with lengths of 8–12 is significantly decreased in the ASD group compared with the TD control group at threshold values of 0.7, 0.8 and 0.9, and there is no significant difference in the number of FC neural circuits with lengths of 4–7 and 13–16 and lengths 16. When the thresholds are 0.7, 0.8, and 0.9, the number of FC neural circuits in some brain regions, such as the right orbital part of the superior frontal gyrus, the left supplementary motor area, the left hippocampus, and the right caudate nucleus, involved in the study is significantly decreased in the ASD group compared with the TD control group. The results of this study indicate that there are significant differences in the FC neural circuits of brain networks in the ASD group compared with the TD control group.

Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female-Male Relationship

Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

Prospective study on resting state functional connectivity in adolescents with major depressive disorder after antidepressant treatment

Recent advances in functional magnetic resonance imaging (fMRI) have resulted in many studies on resting-state functional connectivity (rsFC) in depressed patients. Previous studies have shown alterations between multiple brain areas, such as the prefrontal cortex, anterior cingulate cortex, and basal ganglia, but there are very few prospective studies with a longitudinal design on adolescent depression patients. We therefore investigated the change in positive rsFC in a homogeneous drug-naïve adolescent group after 12 weeks of antidepressant treatment. Functional neuroimaging data were collected and analyzed from 32 patients and 27 healthy controls. Based on previous literature, the amygdala, anterior cingulate cortex (ACC), insula, hippocampus, and dorsolateral prefrontal cortex (DLPFC) were selected as seed regions. Seed-to-voxel analyses were performed between pre- and post-treatment states as well as between the patients and controls at baseline. The positive rsFC between the right DLPFC and the left putamen/right frontal operculum were shown to be higher in patients than in the controls. The positive rsFC between the left DLPFC and left putamen/left lingual gyrus was also higher in the patients than in the controls. The positive rsFC between the right dorsal ACC and the left precentral gyrus had reduced after the 12-week antidepressant treatment. Regions involved in the frontolimbic circuit showed changes in the positive rsFC in the depressed adolescents as compared to in the healthy controls. There were also significant changes in the positive rsFC after 12-weeks of antidepressant treatment. The involved regions were associated with emotional regulation, cognitive functioning, impulse control, and visual processing.

Neural signatures of heterogeneity in risk-taking and strategic consistency

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Abnormal Functional Connectivity of Posterior Cingulate Cortex Correlates With Phonemic Verbal Fluency Deficits in Major Depressive Disorder

Background: Major depressive disorder (MDD) patients face an increased risk of developing cognitive impairments. One of the prominent cognitive impairments in MDD patients is verbal fluency deficit. Nonetheless, it is not clear which vulnerable brain region in MDD is interactively linked to verbal fluency deficit. It is important to gain an improved understanding for verbal fluency deficit in MDD.

Methods: Thirty-four MDD patients and 34 normal controls (NCs) completed resting-state fMRI (rs-fMRI) scan and a set of verbal fluency tests (semantic VFT and phonemic VFT). Fourteen brain regions from five brain networks/systems (central executive network, default mode network, salience network, limbic system, cerebellum) based on their vital role in MDD neuropathology were selected as seeds for functional connectivity (FC) analyses with the voxels in the whole brain. Finally, correlations between the z-score of the FCs from clusters showing significant between-group difference and z-score of the VFTs were calculated using Pearson correlation analyses.

Results: Increased FCs in MDD patients vs. NCs were identified between the bilateral posterior cingulate cortex (PCC) and the right inferior frontal gyrus (triangular part), in which the increased FC between the right PCC and the right inferior frontal gyrus (triangular part) was positively correlated with the z score of phonemic VFT in the MDD patients. Moreover, decreased FCs were identified between the right hippocampal gyrus and PCC, as well as left cerebellum Crus II and right parahippocampal gyrus in MDD patients vs. NCs.

Conclusions: The MDD patients have altered FCs among key brain regions in the default mode network, the central executive network, the limbic system, and the cerebellum. The increased FC between the right PCC and the right inferior frontal gyrus (triangular part) may be useful to better characterize pathophysiology of MDD and functional correlates of the phonemic verbal fluency deficit in MDD.

The Developing Brain in the Digital Era: A Scoping Review of Structural and Functional Correlates of Screen Time in Adolescence

The widespread diffusion of screen-based devices in adolescence has fueled a debate about the beneficial and detrimental effects on adolescents’ well-being and development. With the aim of summarizing the existing literature on the associations between screen time (including Internet-related addictions) and adolescent brain development, the present scoping review summarized evidence from 16 task-unrelated and task-related neuroimaging studies, published between 2010 and 2020. Results highlight three important key messages: (i) a frequent and longer duration of screen-based media consumption (including Internet-related addictive behaviors) is related to a less efficient cognitive control system in adolescence, including areas of the Default Mode Network and the Central Executive Network; (ii) online activities act as strong rewards to the brain and repeated screen time augments the tendency to seek short-term gratifications; and (iii) neuroscientific research on the correlates between screen time and adolescent brain development is still at the beginning and in urgent need for further evidence, especially on the underlying causality mechanisms. Methodological, theoretical, and conceptual implications are discussed.

Reduced default mode network connectivity relative to white matter integrity is associated with poor cognitive outcomes in patients with idiopathic normal pressure hydrocephalus

BACKGROUND

The aim of this study was to investigate whether default mode network (DMN) connectivity and brain white matter integrity at baseline were associated with severe cognitive impairments at baseline and poor cognitive outcomes after shunt placement in patients with idiopathic normal pressure hydrocephalus (iNPH).

METHODS

Twenty consecutive patients with iNPH whose symptoms were followed for 6 months after shunt placement and 10 healthy controls (HCs) were enrolled. DMN connectivity and brain white matter integrity at baseline in the patients with iNPH and HCs were detected by using resting-state functional magnetic resonance imaging (MRI) with independent component analysis and diffusion tensor imaging, respectively, and these MRI indexes were compared between the patients with iNPH and HCs. Performance on neuropsychological tests for memory and executive function and on the gait test was assessed in the patients with iNPH at baseline and 6 months after shunt placement. We divided the patients with iNPH into the relatively preserved and reduced DMN connectivity groups using the MRI indexes for DMN connectivity and brain white matter integrity, and the clinical measures were compared between the relatively preserved and reduced DMN connectivity groups.

RESULTS

Mean DMN connectivity in the iNPH group was significantly lower than that in the HC group and was significantly positively correlated with Rey auditory verbal learning test (RAVLT) immediate recall scores and frontal assessment battery (FAB) scores. Mean fractional anisotropy of the whole-brain white matter skeleton in the iNPH group was significantly lower than that in the HC group. The reduced DMN connectivity group showed significantly worse performance on the RAVLT at baseline and significantly worse improvement in the RAVLT immediate recall and recognition scores and the FAB scores than the preserved DMN connectivity group. Moreover, the RAVLT recognition score highly discriminated patients with relatively preserved DMN connectivity from those with relatively reduced DMN connectivity.

CONCLUSIONS

Our findings indicated that iNPH patients with reduced DMN connectivity relative to the severity of brain white matter disruption have severe memory deficits at baseline and poorer cognitive outcomes after shunt placement. However, further larger-scale studies are needed to confirm these findings.

Alterations in brain structure and function in patients with osteonecrosis of the femoral head: a multimodal MRI study

Background

Pain, a major symptom of osteonecrosis of the femoral head (ONFH), is a complex sensory and emotional experience that presents therapeutic challenges. Pain can cause neuroplastic changes at the cortical level, leading to central sensitization and difficulties with curative treatments; however, whether changes in structural and functional plasticity occur in patients with ONFH remains unclear.

Methods

A total of 23 ONFH inpatients who did not undergo surgery (14 males, nine females; aged 55.61 ± 13.79 years) and 20 controls (12 males, eight females; aged 47.25 ± 19.35 years) were enrolled. Functional indices of the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and a structural index of tract-based spatial statistics (TBSS) were calculated for each participant. The probability distribution of fiber direction was determined according to the ALFF results.

Results

ONFH patients demonstrated increased ALFF in the bilateral dorsolateral superior frontal gyrus, right medial superior frontal gyrus, right middle frontal gyrus, and right supplementary motor area. In contrast, ONFH patients showed decreased ReHo in the left superior parietal gyrus and right inferior temporal gyrus. There were no significant differences in TBSS or probabilistic tractography.

Conclusion

These results indicate cerebral pain processing in ONFH patients. It is advantageous to use functional magnetic resonance imaging to better understand pain pathogenesis and identify new therapeutic targets in ONFH patients.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks

Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on data from the publicly available Human Connectome Project and on a brain tumor dataset acquired at the Johns Hopkins Hospital. We compare our method against feature-based machine learning approaches and a fully-connected deep learning model that does not account for the shared network organization of the data. Our model achieves significantly better performance than competing baselines. We also assess the generalizability and robustness of our method. Our results clearly demonstrate the advantages of our graph convolution architecture combined with multi-task learning and highlight the promise of using rs-fMRI as a presurgical mapping tool.

Transient neural network dynamics in cognitive ageing

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganize and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=595) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain.

Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting

BACKGROUND

This study sought to validate the clinical utility of multimodal magnetic resonance imaging (MRI) techniques in the assessment of neurodegenerative disorders. We intended to demonstrate that advanced neuroimaging techniques commonly used in research can effectively be employed in clinical practice to accurately differentiate heathy aging and dementia subtypes.

METHODS

Twenty patients with dementia of the Alzheimer's type (DAT) and 18 patients with Parkinson's disease dementia (PDD) were identified using gold-standard techniques. Twenty-three healthy, age and sex matched control participants were also recruited. All participants underwent multimodal MRI including T1 structural, diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). MRI modalities were evaluated by trained neuroimaging readers and were separately assessed using cross-validated, iterative discriminant function analyses with subsequent feature reduction techniques. In this way, each modality was evaluated for its ability to differentiate patients with dementia from healthy controls as well as to differentiate dementia subtypes.

RESULTS

Following individual and group feature reduction, each of the multimodal MRI metrics except MRS successfully differentiated healthy aging from dementia and also demonstrated distinct dementia subtypes. Using the following ten metrics, excellent separation (95.5% accuracy, 92.3% sensitivity; 100.0% specificity) was achieved between healthy aging and neurodegenerative conditions: volume of the left frontal pole, left occipital pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, right planum temporale; perfusion of the left hippocampus and left occipital lobe; fractional anisotropy (FA) of the forceps major and bilateral anterior thalamic radiation. Using volume of the left frontal pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, perfusion of the left hippocampus and left occipital lobe; FA of the forceps major and bilateral anterior thalamic radiation, neurodegenerative subtypes were accurately differentiated as well (87.8% accuracy, 95.2% sensitivity; 85.0% specificity).

CONCLUSIONS

Regional volumetrics, DTI metrics, and ASL successfully differentiated dementia patients from controls with sufficient sensitivity to differentiate dementia subtypes. Similarly, feature reduction results suggest that advanced analyses can meaningfully identify brain regions with the most positive predictive value and discriminant validity. Together, these advanced neuroimaging techniques can contribute significantly to diagnosis and treatment planning for individual patients.

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

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.

Visualizing temporal brain-state changes for fMRI using t-distributed stochastic neighbor embedding

Purpose: Currently, functional magnetic resonance imaging (fMRI) is the most commonly used technique for obtaining dynamic information about the brain. However, because of the complexity of the data, it is often difficult to directly visualize the temporal aspect of the fMRI data. Approach: We outline a t -distributed stochastic neighbor embedding (t-SNE)-based postprocessing technique that can be used for visualization of temporal information from a 4D fMRI data. Apart from visualization, we also show its utility in detection of major changes in the brain meta-states during the scan duration. Results: The t-SNE approach is able to detect brain-state changes from task to rest and vice versa for single- and multitask fMRI data. A temporal visualization can also be obtained for task and resting state fMRI data for assessing the temporal dynamics during the scan duration. Additionally, hemodynamic delay can be quantified by comparison of the detected brain-state changes with the experiment paradigm for task fMRI data. Conclusion: The t-SNE visualization can visualize help identify major brain-state changes from fMRI data. Such visualization can provide information about the degree of involvement and attentiveness of the subject during the scan and can be potentially utilized as a quality control for subject's performance during the scan.

Progress of Acupuncture Therapy in Diseases Based on Magnetic Resonance Image Studies: A Literature Review

The neural mechanisms of acupuncture are not well-understood. Over the past decades, an increasing number of studies have used MRI to investigate the response of the brain to acupuncture. The current review aims to provide an update on acupuncture therapy in disease. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from inception to January 31, 2021. Article selection and data extraction were conducted by two review authors. A total of 107 publications about MRI in acupuncture were included, the collective findings of which were as follows: (1) stroke and GB34 (Yanglingquan) are the most studied disease and acupoint. Related studies suggested that the mechanism of acupuncture treatment for stroke may associate with structural and functional plasticity, left and right hemispheres balance, and activation of brain areas related to movement and cognition. GB34 is mainly used in stroke and Parkinson's disease, which mainly activates brain response in the premotor cortex, the supplementary motor area, and the supramarginal gyrus; (2) resting-state functional MRI (rs-fMRI) and functional connectivity (FC) analysis are the most frequently used approaches; (3) estimates of efficacy and brain response to acupuncture depend on the type of sham acupuncture (SA) used for comparison. Brain processing after acupuncture differs between patients and health controls (HC) and occurs mainly in disorder-related areas. Factors that influence the effect of acupuncture include depth of needling, number and locations of acupoints, and deqi and expectation effect, each contributing to the brain response. While studies using MRI have increased understanding of the mechanism underlying the effects of acupuncture, there is scope for development in this field. Due to the small sample sizes, heterogeneous study designs, and analytical methods, the results were inconsistent. Further studies with larger sample sizes, careful experimental design, multimodal neuroimaging techniques, and standardized methods should be conducted to better explain the efficacy and specificity of acupuncture, and to prepare for accurate efficacy prediction in the future.

Electroacupuncture enhances resting-state functional connectivity between dorsal caudate and precuneus and decreases associated leptin levels in overweight/obese subjects

Electroacupuncture (EA) is a safe and effective method for treating obesity. However, how it modulates reward-related brain activity/functional connectivity and gut hormones remains unclear. We employed resting-state functional magnetic resonance imaging (RS-fMRI) and resting-state functional connectivity (RSFC) to investigate EA induced changes in resting-state activity and RSFC in reward-related regions and its association with gut hormones in overweight/obese subjects who received real (n = 20) and Sham (n = 15) stimulation. Results showed reduced leptin levels was positively correlated with reduced body mass index (BMI) and negatively correlated with increased cognitive-control as measured with Three-Factor-Eating-Questionnaire (TFEQ). Significant time effects on RSFC between dorsal caudate (DC) and precuneus were due to significant increased RSFC strength in both EA and Sham groups. In addition, increased RSFC of DC-precuneus was negatively correlated with reduced BMI and leptin levels in the EA group. Mediation analysis showed that the relationship between increased DC-precuneus RSFC strength and reduced BMI was mediated by reduced leptin levels. These findings reflect the association between EA-induced brain reward-related RSFC and leptin levels, and decreased leptin levels mediated altered DC-precuneus RSFC strength and consequent weight-loss, suggesting the potential role of EA in reducing weight and appetite.

Altered regional homogeneity in patients with ovarian cancer treated with chemotherapy: a resting state fMRI study

Many patients treated with chemotherapy for non-central nervous system (CNS) cancers experience cognitive dysfunction. However, few studies have investigated treatment-related neurotoxicity in women with ovarian cancer. The goal of this study was to assess regional brain function in patients with ovarian cancer after first-line chemotherapy. Seventeen patients with ovarian cancer and seventeen healthy controls matched for gender, age and education participated in the study. The patients were evaluated 1-4 months after completion of first line taxane/platinum chemotherapy. All participants underwent resting state functional MRI (rsfMRI) and regional homogeneity (ReHo) indices were calculated. The results showed that patients had significantly decreased average ReHo values in the left middle frontal gyrus, medial prefrontal cortex, and right superior parietal lobule, compared to healthy controls. This is the first rsfMRI study showing ReHo alterations in frontal and parietal regions in patients with ovarian cancer treated with first-line chemotherapy. The findings are overall congruent with prior studies in non-CNS cancer populations and provide supporting evidence for the prevailing notion that frontal areas are particularly vulnerable to the adverse effects of chemotherapy.

Early alterations of large-scale brain networks temporal dynamics in young children with autism

Autism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A-E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain-behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys

OBJECTIVE

The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans.

METHODS

We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis.

RESULTS

Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood.

CONCLUSION

We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.

Patterns of default mode network in temporal lobe epilepsy with and without hippocampal sclerosis

The default mode network (DMN) consists of the deactivation of specific regions during the performance of cognitive tasks and activation during resting or mind wandering. Several pieces of evidence indicate the impairment of DMN in patients with mesial temporal lobe epilepsy (MTLE). However, most of these studies combined different underlying etiologies, failing to disentangle the influence of seizures and presence and side of hippocampal sclerosis (HS). We included 119 patients with MTLE divided into right-HS (n = 42), left-HS (n = 46), and magnetic resonance imaging (MRI)-negative MTLE (n = 31) and controls (n = 59). All underwent resting-state seed-based functional connectivity (FC), with a seed placed at the posterior cingulate cortex (PCC), an essential node for the DMN. To access group inferences, we used an SPM (Statistical Parametric Mapping) full-factorial model to compare patterns of activation using pairwise comparisons among all groups. Our results indicate a different pattern of DMN FC when controlling for side and presence of HS. The group with right-HS had increased FC in the left angular gyrus and the left middle occipital gyrus, when compared to controls, and increased FC of the left hippocampus when compared to the group with left-HS. The MRI-negative group had increased FC of the left hippocampus, left ventral diencephalon, and left fusiform gyrus as compared to left-HS, but did not show any areas of reduced FC compared to controls. By contrast, the group with left-HS did not show areas of increased FC compared to controls or the right-HS and had reduced FC in the left hippocampus compared to controls. Hence, the right-HS presented increased FC in areas related to the DMN in the left hemisphere; the MRI-negative group also showed increased FC in left-sided structures close to temporal lobe when compared to left-HS, probably indicating engagement in a compensatory system. In a subanalysis considering only the MRI-negative with left-sided EEG (electroencephalogram) subgroup, we found differences against controls, with left angular gyrus more connected in the first group, but no significant differences when compared to the group with left-HS. We conclude that the origin of seizures on the left hemisphere seems to engender a less prominent capacity of recruiting other neighbor areas related to DMN as compared to right-HS and controls. Considering recent studies that have revealed the importance of DMN for cognitive skills and memory, our findings may indicate that deficiencies exhibited by patients with left-HS temporal lobe epilepsy (TLE) in connecting to the DMN could be a surrogate marker of their known worse neuropsychological performance. Further studies with direct comparisons between cognitive tests and FC within the DMN are needed to validate these findings, especially for MRI-negative patients. This article is part of the Special Issue "NEWroscience 2018".

The development of theta and alpha neural oscillations from ages 3 to 24 years

Intrinsic, unconstrained neural activity exhibits rich spatial, temporal, and spectral organization that undergoes continuous refinement from childhood through adolescence. The goal of this study was to investigate the development of theta (4-8 Hertz) and alpha (8-12 Hertz) oscillations from early childhood to adulthood (years 3-24), as these oscillations play a fundamental role in cognitive function. We analyzed eyes-open, resting-state EEG data from 96 participants to estimate genuine oscillations separately from the aperiodic (1/f) signal. We examined age-related differences in the aperiodic signal (slope and offset), as well as the peak frequency and power of the dominant posterior oscillation. For the aperiodic signal, we found that both the aperiodic slope and offset decreased with age. For the dominant oscillation, we found that peak frequency, but not power, increased with age. Critically, early childhood (ages 3-7) was characterized by a dominance of theta oscillations in posterior electrodes, whereas peak frequency of the dominant oscillation in the alpha range increased between ages 7 and 24. Furthermore, theta oscillations displayed a topographical transition from dominance in posterior electrodes in early childhood to anterior electrodes in adulthood. Our results provide a quantitative description of the development of theta and alpha oscillations.

Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment

Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.

Revisiting social MPE: an integration of molecular pathological epidemiology and social science in the new era of precision medicine

INTRODUCTION

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary area examining the relationships between various exposures and pathogenic signatures of diseases. In line with the accelerating advancements in MPE, social science and its health-related interdisciplinary areas have also developed rapidly. Accumulating evidence indicates the pathological role of social-demographic factors. We therefore initially proposed social MPE in 2015, which aims to elucidate etiological roles of social-demographic factors and address health inequalities globally. With the ubiquity of molecular diagnosis, there are ample opportunities for researchers to utilize and develop the social MPE framework.

AREAS COVERED

Molecular subtypes of breast cancer have been investigated rigorously for understanding its etiologies rooted from social factors. Emerging evidence indicates pathogenic heterogeneity of neurological disorders such as Alzheimer's disease. Presenting specific patterns of social-demographic factors across different molecular subtypes should be promising for advancing the screening, prevention, and treatment strategies of those heterogeneous diseases. This article rigorously reviewed literatures investigating differences of race/ethnicity and socioeconomic status across molecular subtypes of breast cancer and Alzheimer's disease to date.

EXPERT OPINION

With advancements of the multi-omics technologies, we foresee a blooming of social MPE studies, which can address health disparities, advance personalized molecular medicine, and enhance public health.

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

Altered hippocampal functional connectivity patterns in patients with cognitive impairments following ischaemic stroke: A resting-state fMRI study

BACKGROUND

Ischemic stroke with cognitive impairment is a considerable risk factor for developing dementia. Identifying imaging markers of cognitive impairment following ischemic stroke will help to develop prevention strategies against post-stroke dementia.

METHODS

We investigated the hippocampal functional connectivity (FC) pattern following ischemic stroke, using resting-state fMRI (rs-fMRI). Thirty-three cognitively impaired patients after ischemic stroke and sixteen age-matched controls with no known history of neurological disorder were recruited for the study. No patient had a direct ischaemic insult to hippocampus on the examination of brain imaging. Seven subfields of hippocampus were used as seeds region for FC analyses.

RESULTS

Across all hippocampal subfields, FC with the inferior parietal lobule was reduced in stroke patients as compared with healthy controls. This decreased FC included both supramarginal gyrus and angular gyrus. The FC of hippocampal subfields with cerebellum was increased. Importantly, the degree of the altered FC between hippocampal subfields and inferior parietal lobule was associated with their impaired memory function.

CONCLUSION

Our results demonstrated that decreased hippocampal-inferior parietal lobule connectivity was associated with cognitive impairment in patients with ischemic stroke. These findings provide novel insights into the role of hippocampus in cognitive impairment following ischemic stroke.

Developmental heatmaps of brain functional connectivity from newborns to 6-year-olds

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Heatmaps quantify degrees of developmental changes in functional connectivity.

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More changes are observed in the first than second postnatal year, driven by girls.

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Most change is observed from ages 2–4 compared with any other age span.

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Limbic and subcortical areas show more changes than primary sensory regions.

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Consistent trajectories of functional connectivity are found across validations.

Different functional networks exhibit distinct longitudinal trajectories throughout development, but the timeline of the dynamics of functional connectivity across the whole brain remains to be elucidated. Here we used resting-state fMRI to investigate the development of voxel-level changes in functional connectivity across the first six years of life. Globally, we found that developmental changes in functional connectivity are nonlinear with more changes during the first postnatal year than the second, followed by most significant changes from ages 2–4 and from ages 4–6. However, the overall global difference observed between the first and second year appears to have been driven by girls. Limbic and subcortical areas consistently demonstrated the most substantial changes, whereas primary sensory areas were the most stable. These patterns were consistent in full-term and preterm subgroups. Validation on randomly divided subsamples as well as in an independent cross-sectional sample revealed global patterns consistent with the main results. Overall, the derived developmental heatmaps reveal novel dynamics underlying functional circuit development during the first 6 years of life.

Age-related hearing loss and cognitive decline: MRI and cellular evidence

Extensive evidence supports the association between age-related hearing loss (ARHL) and cognitive decline. It is, however, unknown whether a causal relationship exists between these two, or whether they both result from shared mechanisms. This paper intends to study this relationship through a comprehensive review of MRI findings as well as evidence of cellular alterations. Our review of structural MRI studies demonstrates that ARHL is independently linked to accelerated atrophy of total and regional brain volumes and reduced white matter integrity. Resting-state and task-based fMRI studies on ARHL also show changes in spontaneous neural activity and brain functional connectivity; and alterations in brain areas supporting auditory, language, cognitive, and affective processing independent of age, respectively. Although MRI findings support a causal relationship between ARHL and cognitive decline, the contribution of potential shared mechanisms should also be considered. In this regard, the review of cellular evidence indicates their role as possible common mechanisms underlying both age-related changes in hearing and cognition. Considering existing evidence, no single hypothesis can explain the link between ARHL and cognitive decline, and the contribution of both causal (i.e., the sensory hypothesis) and shared (i.e., the common cause hypothesis) mechanisms is expected.

The effect of body posture on resting-state functional connectivity

INTRODUCTION

An important but under-investigated confound of functional MRI (fMRI) is body posture. Although it is well-established that body position changes cerebral blood flow, the amount of cerebrospinal fluid in the brain, intracranial pressure, and even the firing rate of certain cell types, there is currently no study that directly examines its effect on fMRI measurements. Moreover, fMRI is typically done in a supine position, which often does not correspond to how these processes are performed in everyday settings.

METHODS

In this study, 20 healthy adults underwent resting-state fMRI under three body positions: supine, right lateral decubitus (RLD), and left lateral decubitus (LLD). We first investigated whether there were differences in overall organization of whole-brain connectivity between conditions using graph theory. Second, we examined whether functional connectivity of two most studied default mode network (DMN) seeds to the rest of the brain was altered as a function of body position.

RESULTS

Nonparametric statistical analyses revealed that global network measures differed among conditions, with the supine and LLD showing identical results compared to the RLD. There was decreased connectivity for DMN seeds in the RLD condition compared to the supine and LLD, but there were no significant differences between the latter two conditions.

DISCUSSION

Potential mechanisms underlying these alterations include gravity, changes in physiology, and body anatomy. Our results suggest that, compared to supine and LLD, the RLD position leads to changes in whole-brain and DMN connectivity. Finally, depending on the research question, combining imaging modalities that allow for more naturalistic settings can provide a better understanding of certain phenomena.

Myths and truths about pediatric psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) is a neuropsychiatric condition that causes a transient alteration of consciousness and loss of self-control. PNES, which occur in vulnerable individuals who often have experienced trauma and are precipitated by overwhelming circumstances, are a body's expression of a distressed mind, a cry for help. PNES are misunderstood, mistreated, under-recognized, and underdiagnosed. The mindbody dichotomy, an artificial divide between physical and mental health and brain disorders into neurology and psychiatry, contributes to undue delays in the diagnosis and treatment of PNES. One of the major barriers in the effective diagnosis and treatment of PNES is the dissonance caused by different illness perceptions between patients and providers. While patients are bewildered by their experiences of disabling attacks beyond their control or comprehension, providers consider PNES trivial because they are not epileptic seizures and are caused by psychological stress. The belief that patients with PNES are feigning or controlling their symptoms leads to negative attitudes of healthcare providers, which in turn lead to a failure to provide the support and respect that patients with PNES so desperately need and deserve. A biopsychosocial perspective and better understanding of the neurobiology of PNES may help bridge this great divide between brain and behavior and improve our interaction with patients, thereby improving prognosis. Knowledge of dysregulated stress hormones, autonomic nervous system dysfunction, and altered brain connectivity in PNES will better prepare providers to communicate with patients how intangible emotional stressors could cause tangible involuntary movements and altered awareness.

Abnormalities of intrinsic brain activity in irritable bowel syndrome (IBS): A protocol for systematic review and meta analysis of resting-state functional imaging

BACKGROUND

Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal (GI) disorders affecting up to 11.5% of the general global population. The brain-gut axis has been shown to play an important role in the pathogenesis of IBS. Several studies confirmed that intrinsic brain abnormalities existed in patients with IBS. But, studies of abnormal regional homogeneity (ReHo) in IBS have reported inconsistent results. The objective of this protocol is to conduct a meta-analysis using the Seed-based d mapping software package to identify the most consistent and replicable findings of ReHo in IBS patients.

METHOD

We will search the following three electronic databases: MEDLINE, EMBASE and Web of Science. The primary outcome will include the peak coordinates and effect sizes of differences in ReHo between patients with IBS and healthy controls from each dataset. The secondary outcomes will be the effects of age, illness severity, illness duration, and scanner field strength. The SDM approach was used to conduct voxel-wise meta-analysis. Whole-brain voxel-based jackknife sensitivity analysis was performed to conduct jackknife sensitivity analysis. A random effects model with Q statistics is used to conduct heterogeneity and publication bias between studies and meta-regression analyses were carried out to examine the effects of age, illness severity, illness duration, and scanner field strength.

RESULTS

The results of this paper will be submitted to a peer-reviewed journal for publication.

CONCLUSION

This research will determine the consistent pattern of alterations in ReHo in IBS patients.

Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization

In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple clustering method is a powerful analytical tool, which identifies clustering patterns of subjects depending on their FC in specific brain areas. However, when one applies an existing multiple clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i.e., vectorizing a correlation matrix. Such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple clustering method based on Wishart mixture models, which preserves the correlation matrix structure without vectorization. The uniqueness of this method is that the multiple clustering of subjects is based on particular networks of nodes (or regions of interest, ROIs), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI sub-network. The key assumption of the method is independence among sub-networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.

New analysis method for functional brain imaging: White noise removed T2* variation mapping using multi-echo EPI

BACKGROUND

Generally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T₂*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches.

NEW METHOD

We derived the standard deviation to the mean-square ratio of the true T₂* signal from the multi-echo EPI (ME-EPI) dataset. For the true T₂*-variation-based value, we removed the S₀ (initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset.

RESULTS

The activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S₀ component removed.

COMPARISON WITH EXISTING METHODS

Conventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied.

CONCLUSIONS

We demonstrated white noise-removed true T₂*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.

Brain connectivity alterations after additional sensorimotor or motor therapy for the upper limb in the early-phase post stroke: a randomized controlled trial

Somatosensory function plays an important role for upper limb motor learning. However, knowledge about underlying mechanisms of sensorimotor therapy is lacking. We aim to investigate differences in therapy-induced resting-state functional connectivity changes between additional sensorimotor compared with motor therapy in the early-phase post stroke. Thirty first-stroke patients with a sensorimotor impairment were included for an assessor-blinded multi-centre randomized controlled trial within 8 weeks post stroke [13 (43%) females; mean age: 67 ± 13 years; mean time post stroke: 43 ± 13 days]. Patients were randomly assigned to additional sensorimotor (n = 18) or motor (n = 12) therapy, receiving 16 h of additional therapy within 4 weeks. Sensorimotor evaluations and resting-state functional magnetic resonance imaging were performed at baseline (T1), post-intervention (T2) and after 4 weeks follow-up (T3). Resting-state functional magnetic resonance imaging was also performed in an age-matched healthy control group (n = 19) to identify patterns of aberrant connectivity in stroke patients between hemispheres, or within ipsilesional and contralesional hemispheres. Mixed model analysis investigated session and treatment effects between stroke therapy groups. Non-parametric partial correlations were used to investigate brain−behaviour associations with age and frame-wise displacement as nuisance regressors. Connections within the contralesional hemisphere that showed hypo-connectivity in subacute stroke patients (compared with healthy controls) showed a trend towards a more pronounced pre-to-post normalization (less hypo-connectivity) in the motor therapy group, compared with the sensorimotor therapy group (mean estimated difference = −0.155 ± 0.061; P = 0.02). Further, the motor therapy group also tended to show a further pre-to-post increase in functional connectivity strength among connections that already showed hyper-connectivity in the stroke patients at baseline versus healthy controls (mean estimated difference = −0.144 ± 0.072; P = 0.06). Notably, these observed increases in hyper-connectivity of the contralesional hemisphere were positively associated with improvements in functional activity (r = 0.48), providing indications that these patterns of hyper-connectivity are compensatory in nature. The sensorimotor and motor therapy group showed no significant differences in terms of pre-to-post changes in inter-hemispheric connectivity or ipsilesional intrahemispheric connectivity. While effects are only tentative within this preliminary sample, results suggest a possible stronger normalization of hypo-connectivity and a stronger pre-to-post increase in compensatory hyper-connectivity of the contralesional hemisphere after motor therapy compared with sensorimotor therapy. Future studies with larger patient samples are however recommended to confirm these trend-based preliminary findings.

Resting activity of the hippocampus and amygdala in obese individuals predicts their response to food cues

Obese individuals exhibit brain functional abnormalities in multiple regions implicated in reward/motivation, emotion/memory, homeostatic regulation, and executive control when exposed to food cues and during rest. However, it remains unclear whether abnormal brain responses to food cues might account for or relate to their abnormal activity in resting state. This information would be useful for understanding the neural mechanisms behind hyperactive responses to food cues, a critical marker of obesity. Resting-state functional magnetic resonance imaging (RS-fMRI) and a cue-reactivity fMRI task with high- (HiCal) and low-caloric (LoCal) food cues were employed to investigate brain baseline activity and food cue-induced activation differences in 44 obese participants (OB), in 37 overweight participants (OW), and in 37 normal weight (NW) controls. One-way analyses of variance showed there was a group difference in the left hippocampus/amygdala activity during resting state and during food-cue stimulation (p_FWE < 0.05); post-hoc tests showed the OB group had both greater basal activity and greater food cue-induced activation than the OW and NW groups; OW had higher activity in the hippocampus/amygdala than the NW group, which was only significant during resting state. In the OB group, resting-state activity in the left hippocampus/amygdala was positively correlated with activation induced by HiCal food cues, and both of these measures correlated with body mass index (BMI). Mediation analysis showed that the relationship between BMI and hippocampus/amygdala response to HiCal food cues was mediated by their resting-state activity. These findings suggest a close association between obesity and brain functional abnormality in the hippocampus/amygdala. They also indicate that resting-state activity in the hippocampus/amygdala may impact these regions' responses to food cues.

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

Resting state differences between successful and unsuccessful restrained eaters

Restrained eating is a popular weight loss strategy for young women that tends to have limited effectiveness over extended periods of time. Although previous studies have explored and identified possible personality and behavior differences between successful and unsuccessful restrained eaters (REs), there has been a paucity of research on neurophysiological differences.Towards addressing this gap, we assessed brain resting state (Rs) differences in groups of unsuccessful REs (N = 39) and successful REs (N = 31). In line with hypotheses, unsuccessful REs displayed reduced regional homogeneity in brain regions involved in cognitive control (inferior parietal lobe) compared to successful REs. Regions involved in conflict monitoring (anterior cingulate cortex) were also observed to be comparatively less active in the unsuccessful RE group. Finally, based on analyses of independent components and seed-based functional connectivity, regions involved in conflict monitoring and cognitive control, especially those localized within the frontoparietal network, showed weaker connectivities among unsuccessful REs compared to their successful counterparts.These results underscore specific brain Rs differences between successful REs and unsuccessful REs in regions implicated in cognitive control and conflict monitoring.

Modeling motor task activation from resting-state fMRI using machine learning in individual subjects

Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications.

Chemotherapy-induced functional changes of the default mode network in patients with lung cancer

Previous studies have demonstrated that cognitive impairment is associated with neurophysiological changes in lung cancer following chemotherapy. This study aimed to investigate the intrinsic functional connectivity (FC) pattern within the default mode network (DMN) and its associations with cognitive impairment in patients with lung cancer revealed by resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI scans were acquired from 21 post-chemotherapy and 27 non-chemotherapy lung cancer patients and 30 healthy controls. All groups were age, gender and education-matched. The posterior cingulate cortex (PCC) was chosen as the seed region to detect the FC patterns and then determine whether these changes were related with specific cognitive performance. Compared with non-chemotherapy lung cancer patients, chemotherapy patients revealed decreased FC between the PCC and the right anterior cingulate cortex (ACC), left inferior parietal lobule (IPL), and left medial prefrontal cortex (mPFC), as well as increased FC with the left postcentral gyrus (PoCG). Relative to healthy controls, post-chemotherapy patients exhibited reduced FC between the PCC and the left ACC and left temporal lobe, as well as increased FC with the right PoCG. Moreover, the decreased FC of the PCC to bilateral ACC in post-chemotherapy patients was positively associated with reduced MoCA scores (left: r = 0.529, p = 0.029; right: r = 0.577, p = 0.015). The current study mainly demonstrated reduced resting-state FC pattern within the DMN regions that was linked with impaired cognitive function in lung cancer patients after chemotherapy. These findings illustrated the potential role of the DMN in lung cancer patients that will provide novel insight into the underlying neuropathological mechanisms in chemotherapy-induced cognitive impairment.