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

Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.

Sustained effects of single doses of classical psychedelics in humans

The serotonergic classical psychedelics include compounds that primarily activate the brain's serotonin 2 A receptor (5-HT2AR), such as LSD, psilocybin, and DMT (ayahuasca). The acute effects of these compounds are well-known as are their ability to increase the emotional state both in healthy people and in those with neuropsychiatric disorders. In particular psilocybin, the psychoactive constituent in "magic mushrooms", has shown great potential for treatment of anxiety and depression. A unique and compelling feature of psychedelics is that intake of just a single psychedelic dose is associated with long-lasting effects. This includes effects on personality, e.g., higher openness, and amelioration of depressive symptoms. This review focuses on these stunning effects and summarizes our current knowledge on which behavioral, biochemical, neuroimaging, and electrophysiological data support that the intriguing effects of psychedelics on the human brain and mind are based on neural plasticity. The review also points to so far understudied areas and suggests research questions to be addressed in future studies which potentially can help to understand the intriguing long-term effects after intake of a single (or a few) psychedelic doses.

Case Report: Zolpidem's paradoxical restorative action: A case report of functional brain imaging

Zolpidem is a sedative drug that has been shown to induce a paradoxical effect, restoring brain function in wide range of neurological disorders. The underlying functional mechanism of the effect of zolpidem in the brain in clinical improvement is still poorly understood. Thus, we aimed to investigate rest brain function to study zolpidem-induced symptom improvement in a patient who developed postoperative pediatric cerebellar mutism syndrome, a postoperative complication characterized by delayed onset transient mutism/reduced speech that can occur after medulloblastoma resection. The patient experienced clinical recovery after a single dose of zolpidem. Brain function was investigated using arterial spin labeling MRI and resting-state functional MRI. Imaging was performed at three time-points: preoperative, postoperative during symptoms, and after zolpidem intake when the symptoms regressed. Whole brain rest cerebral blood flow (CBF) and resting state functional connectivity using Pearson coefficient correlations between pairs of regions of interest were investigated two-by-two at the different time points. A comparison between postoperative and preoperative images showed a significant decrease in rest CBF in the left supplementary motor area, Broca's area, and the left striatum and a decrease in functional connectivity within the dentato-thalamo-cortical and cortico-striato-pallido-thalamo-cortical loops. Post-zolpidem images showed increased CBF in the left striatum and increased functional connectivity within the disrupted loops relative to postoperative images. Thus, we observed functional changes within the broader speech network and thalamo-subcortical interactions associated with the paradoxical effect of zolpidem in promoting clinical recovery. This should encourage further functional investigations in the brain to better understand the mechanism of zolpidem in neurological recovery.

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

BACKGROUND

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.

OBJECTIVES

Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.

METHODS

A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.

RESULTS

Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.

CONCLUSION

Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

A resting-state functional magnetic resonance imaging study of altered functional brain activity in cardiac arrest survivors with good neurological outcome

Purpose

To investigate the spontaneous brain activity alterations in survivors of cardiac arrest (CA) with good neurological outcome using resting-state functional magnetic resonance imaging (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) methods.

Materials and methods

Thirteen CA survivors with favorable neurological outcomes and 13 healthy controls (HCs) were recruited and underwent rs-fMRI scans. The ALFF and ReHo methods were applied to assess the regional intensity and synchronization of spontaneous brain activity. Correlation analyses were performed to explore the relationships between the mean ALFF and ReHo values in significant clusters and clinical parameters.

Results

The survivors of CA showed significantly decreased ALFF values in the left postcentral gyrus and precentral gyrus and increased ALFF values in the left hippocampus and parahippocampal gyrus than HCs. Significantly decreased ReHo values were observed in the left inferior occipital gyrus and middle occipital gyrus in the patients. Mean ALFF values in the left hippocampus and parahippocampal gyrus were positively correlated with the time to return of spontaneous circulation (r = 0.794, p = 0.006) in the patient group.

Conclusion

Functional activity alterations in the brain areas corresponding to known cognitive and physical impairments were observed in CA survivors with preserved neurological function. Our results could advance the understanding of the neurological mechanisms underlying the residual deficits in those patients.

Central Nervous System Involvement in Primary Sjögren's Syndrome: Narrative Review of MRI Findings

Central nervous system (CNS) involvement is one of the numerous extraglandular manifestations of primary Sjögren's syndrome (pSS). Moreover, neurological complaints precede the sicca symptoms in 25-60% of the cases. We review the magnetic resonance imaging (MRI) lesions typical for pSS, involving the conventional examination, volumetric and morphometric studies, diffusion tensor imaging (DTI) and resting-state fMRI. The most common radiological lesions in pSS are white matter hyperintensities (WMH), scattered alterations hyperlucent on T2 and FLAIR sequences, typically located periventricularly and subcortically. Cortical atrophy and ventricular dilatation can also occur in pSS. Whilst these conditions are thought to be more common in pSS than healthy controls, DTI and resting-state fMRI alterations demonstrate evident microstructural changes in pSS. As pSS is often accompanied by cognitive symptoms, these MRI alterations are expectedly related to them. This relationship is not clearly delineated in conventional MRI studies, but DTI and resting-state fMRI examinations show more convincing correlations. In conclusion, the CNS manifestations of pSS do not follow a certain pattern. As the link between the MRI lesions and clinical manifestations is not well established, more studies involving larger populations should be performed to elucidate the correlations.

EEG-Based Mapping of Resting-State Functional Brain Networks in Patients with Parkinson's Disease

(1) Background: Directed functional connectivity (DFC) alterations within brain networks are described using fMRI. EEG has been scarcely used. We aimed to explore changes in DFC in the sensory-motor network (SMN), ventral-attention network (VAN), dorsal-attention network (DAN), and central-executive network (CEN) using an EEG-based mapping between PD patients and healthy controls (HCs). (2) Methods: Four-minutes resting EEG was recorded from 29 PD patients and 28 HCs. Network’s hubs were defined using fMRI-based binary masks and their electrical activity was calculated using the LORETA. DFC between each network’s hub-pairs was calculated for theta, alpha and beta bands using temporal partial directed coherence (tPDC). (3) Results: tPDCs percent was lower in the CEN and DAN in PD patients compared to HCs, while no differences were observed in the SMN and VAN (group*network: F = 5.943, p < 0.001) in all bands (group*band: F = 0.914, p = 0.401). However, in the VAN, PD patients showed greater tPDCs strength compared to HCs (p < 0.001). (4) Conclusions: Our results demonstrated reduced connectivity in the CEN and DAN, and increased connectivity in the VAN in PD patients. These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD.

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.

Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10 months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life.

Regional brain dysfunction in insomnia after ischemic stroke: A resting-state fMRI study

Objective

This study aimed to explore the abnormality of local brain function in patients with post-stroke insomnia (PSI) based on fMRI and explore the possible neuropathological mechanisms of insomnia in patients with PSI in combination with the Pittsburgh sleep quality index (PSQI) score and provide an objective evaluation index for the follow-up study of acupuncture treatment of PSI.

Methods

A total of 27 patients with insomnia after stroke were enrolled, and the PSQI was used to evaluate their sleep status. Twenty-seven healthy participants who underwent physical examinations during the same period were selected as controls. Resting-state brain function images and structural images of the two groups of participants were collected, and the abnormal changes in the regional brain function in patients with PSI were analyzed using three methods: regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF), and a correlation analysis with the PSQI scale score.

Results

Compared with the HCs, the ReHo values of the PSI group in the bilateral lingual gyrus, right cuneus, right precentral and postcentral gyri were significantly lower, and the ReHo values of the left supramarginal gyrus were significantly higher. In the PSI group, the ALFF values in the bilateral lingual gyrus were significantly decreased, whereas those in the bilateral middle temporal gyrus, right inferior temporal gyrus, right inferior frontal gyrus, right limbic lobe, right precuneus, left posterior cingulate gyrus, and left middle occipital gyrus were significantly increased. Compared with HCs, the fALFF values of the bilateral lingual gyrus, bilateral inferior occipital gyrus, and bilateral cuneus in the PSI group were significantly higher. The ReHo value of the left supramarginal gyrus in the PSI group was significantly negatively correlated with the total PSQI score.

Conclusion

Patients with PSI have abnormal local activities in multiple brain regions, including the visual processing-related cortex, sensorimotor cortex, and some default-mode network (DMN) regions. Over-arousal of the DMN and over-sensitivity of the audiovisual stimuli in patients with PSI may be the main mechanisms of insomnia and can lead to a decline in cognitive function and abnormalities in emotion regulation simultaneously.

Altered spontaneous neural activity in experimental odontogenic pain: a resting-state functional MRI study

OBJECTIVE

This study aimed to evaluate the intrinsic cerebral activity alternations in experimental odontogenic pain with resting-state functional magnetic resonance imaging (fMRI).

MATERIALS AND METHODS

Forty-nine participants in an odontogenic pain group and 49 participants in control group underwent imaging using fMRI in this prospective study. Odontogenic pain was induced by experimental tooth movement. We calculated the fractional amplitude of low-frequency fluctuation (fALFF) value to evaluate regional cerebral function and compared it between the two groups utilizing a voxel-based two-sample t-test.

RESULTS

In comparison with the healthy controls, the participants in odontogenic pain group showed increased fALFF value in the left cerebellum, right posterior cingulate gyrus, and bilateral inferior temporal gyrus, as well as decreased fALFF in the medial prefrontal cortex, the left anterior cingulate cortex, bilateral angular gyrus, left inferior parietal cortex, middle temporal gyrus, and miscellaneous cerebral regions (P < 0.001 familywise error-corrected VOXEL > 100).

CONCLUSION

The present study showed abnormal cerebral activity in odontogenic pain, and reveled that the aberrant regional functional activities were mainly located within the default mode network. The finding could provide insight into the underlying neural mechanism of odontogenic pain. Registry of clinical trials (Trial number ChiCTR1800018589) - http://www.chictr.org.cn/showproj.aspx?proj=31424.

Neuroimaging in schizophrenia: A review article

In this review article we have consolidated the imaging literature of patients with schizophrenia across the full spectrum of modalities in radiology including computed tomography (CT), morphologic magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and magnetoencephalography (MEG). We look at the impact of various subtypes of schizophrenia on imaging findings and the changes that occur with medical and transcranial magnetic stimulation (TMS) therapy. Our goal was a comprehensive multimodality summary of the findings of state-of-the-art imaging in untreated and treated patients with schizophrenia. Clinical imaging in schizophrenia is used to exclude structural lesions which may produce symptoms that may mimic those of patients with schizophrenia. Nonetheless one finds global volume loss in the brains of patients with schizophrenia with associated increased cerebrospinal fluid (CSF) volume and decreased gray matter volume. These features may be influenced by the duration of disease and or medication use. For functional studies, be they fluorodeoxyglucose positron emission tomography (FDG PET), rs-fMRI, task-based fMRI, diffusion tensor imaging (DTI) or MEG there generally is hypoactivation and disconnection between brain regions. However, these findings may vary depending upon the negative or positive symptomatology manifested in the patients. MR spectroscopy generally shows low N-acetylaspartate from neuronal loss and low glutamine (a neuroexcitatory marker) but glutathione may be elevated, particularly in non-treatment responders. The literature in schizophrenia is difficult to evaluate because age, gender, symptomatology, comorbidities, therapy use, disease duration, substance abuse, and coexisting other psychiatric disorders have not been adequately controlled for, even in large studies and meta-analyses.

Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders

Introduction

Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging.

Materials and methods

This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile.

Results

Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe.

Conclusion

Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.

Cognitive theories of autism based on the interactions between brain functional networks

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5–14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.

Acute tryptophan depletion balances altered resting-state functional connectivity of the salience network in female patients recovered from anorexia nervosa

BACKGROUND

It has been suggested that individuals predisposed to or recovered from anorexia nervosa experience a hyperserotonergic state associated with anxiety that might be mitigated by restricted food intake, because diminished levels of the tryptophan precursor lower the central availability of serotonin (5-HT). At the neural level, the salience network is a system of functionally connected brain regions; it has been closely associated with 5-HT functioning and mental disorders (including anorexia nervosa). The aim of the present study was to investigate the effect on the salience network of a temporary dietary manipulation of 5-HT synthesis in patients with anorexia nervosa.

METHODS

In this double-blind crossover study, we obtained data on resting-state functional connectivity from 22 weight-recovered female patients with a history of anorexia nervosa, and 22 age-matched female healthy controls. The study procedure included acute tryptophan depletion (a dietary intervention that lowers the central 5-HT synthesis rate) and a sham condition.

RESULTS

We identified an interaction of group and experimental condition in resting-state functional connectivity between the salience network and the orbitofrontal cortex extending to the frontal pole (F _1,42 = 12.52; p _FWE = 0.026). Further analysis revealed increased resting-state functional connectivity during acute tryptophan depletion in patients recovered from anorexia nervosa, resembling that of healthy controls during the sham condition (T ₄₂ = -0.66; p = 0.51).

LIMITATIONS

The effect of acute tryptophan depletion on the central availability of 5-HT can be judged only indirectly using plasma ratios of tryptophan to large neutral amino acids. Moreover, the definition of anorexia nervosa recovery varies widely across studies, limiting comparability.

CONCLUSION

Taken together, our findings support the notion of 5-HT dysregulation in anorexia nervosa and indicate that reduced 5-HT synthesis and availability during acute tryptophan depletion (and possibly with food restriction) may balance hyperserotonergic functioning and the associated resting-state functional connectivity of the salience network.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls

Purpose

Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer's disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer's disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.

Methods

We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies-Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.

Results

We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04).

Conclusion

We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.

A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data

Addiction to methamphetamine (MA) is a major public health concern. Developing a predictive model that can classify and characterize the brain-based biomarkers predicting MA addicts may directly lead to improved treatment outcomes. In the current study, we applied the support vector machine (SVM)-based classification method to resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from individuals with methamphetamine use disorder (MUD) and healthy controls (HCs) to identify brain-based features predictive of MUD. Brain connectivity analyses were conducted for 36 individuals with MUD as well as 37 HCs based on the brainnetome atlas, and the neighborhood component analysis was applied for feature selection. Eighteen most relevant features were screened out and fed into the SVM to classify the data. The classifier was able to differentiate individuals with MUD from HCs with a high prediction accuracy, sensitivity, specificity, and AUC of 88.00, 86.84, 89.19, and 0.94, respectively. The top six discriminative features associated with changes in the functional activity of key nodes in the default mode network (DMN), all the remaining discriminative features are related to the thalamic connections within the cortico-striato-thalamo-cortical (CSTC) loop. In addition, the functional connectivity (FC) between the bilateral inferior parietal lobule (IPL) and right cingulate gyrus (CG) was significantly correlated with the duration of methamphetamine use. The results of this study not only indicated that MUD-related FC alterations were predictive of group membership, but also suggested that machine learning techniques could be used for the identification of MUD-related imaging biomarkers.

Extracting default mode network based on graph neural network for resting state fMRI study

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.

A spinal manipulative therapy altered brain activity in patients with lumbar disc herniation: A resting-state functional magnetic resonance imaging study

Purpose

Lumbar disc herniation (LDH) is one of the leading causes of low-back pain and results in a series of clinical symptoms, including pain, reflex loss, and muscle weakness. Spinal manipulative therapy (SMT) can relieve pain and promote internal and external stabilization of the lumbar spine. In this study, we investigated whether the brain alterations of LDH patients with SMT were frequency-dependent based on the calculation of Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF (fALFF). Further, we established a cohort of LDH patients to evaluate the contribution of SMT treatments to brain functional reorganization.

Methods

A total of 55 participants, including 27 LDH patients and 28 health controls (HCs), were collected. All LDH patients underwent two fMRI scans (before SMT and after the sixth SMT session). To represent LDH-related brain oscillatory activities, we calculated the ALFF and fALFF in the conventional band (0.01-0.08 Hz), the slow-4 band (0.027-0.073 Hz), and the slow-5 band (0.01-0.027 Hz). Moreover, we extracted ALFF and fALFF values in clusters with significant differences to evaluate the SMT effect.

Results

Compared with HCs, the LDH patients before SMT (LDH-pre) exhibited increased fALFF in right lingual gyri in the conventional band, and showed increased fALFF in left Cerebelum_Crus1 in the slow-4 band. We further examined the abnormal brain activities changes before and after the SMT intervention. The ALFF and fALFF values of LDH-pre group were higher than those of the HCs and LDH-pos groups. After SMT, the increased ALFF and fALFF values were suppressed for patients in conventional band and slow-4 band.

Conclusion

The present study characterized the altered regional patterns in spontaneous neural activity in patients with LDH. Meanwhile, SMT is an effective treatment of LDH, and we supposed that it might have been involved in modulating dysfunctional brain regions which are important for the processing of pain. The findings of the current study may provide new insights to understand pathological mechanism of LDH.

Divergences Between Resting State Networks and Meta-Analytic Maps Of Task-Evoked Brain Activity

Background:

Spontaneous human neural activity is organized into resting state networks, complex patterns of synchronized activity that account for the major part of brain metabolism. The correspondence between these patterns and those elicited by the performance of cognitive tasks would suggest that spontaneous brain activity originates from the stream of ongoing cognitive processing.

Objective:

To investigate a large number of meta-analytic activation maps obtained from Neurosynth (www.neurosynth.org), establishing the extent of task-rest similarity in large-scale human brain activity.

Methods:

We applied a hierarchical module detection algorithm to the Neurosynth activation map similarity network, and then compared the average activation maps for each module with a set of resting state networks by means of spatial correlations.

Results:

We found that the correspondence between resting state networks and task-evoked activity tended to hold only for the largest spatial scales. We also established that this correspondence could be biased by the inclusion of maps related to neuroanatomical terms in the database (e.g. “parietal”, “occipital”, “cingulate”, etc.).

Conclusion:

Our results establish divergences between brain activity patterns related to spontaneous cognition and the spatial configuration of RSN, suggesting that anatomically-constrained homeostatic processes could play an important role in the inception and shaping of human resting state activity fluctuations.

The comorbidity and cognition in multiple sclerosis (CCOMS) neuroimaging protocol: Study rationale, MRI acquisition, and minimal image processing pipelines

The Comorbidity and Cognition in Multiple Sclerosis (CCOMS) study represents a coordinated effort by a team of clinicians, neuropsychologists, and neuroimaging experts to investigate the neural basis of cognitive changes and their association with comorbidities among persons with multiple sclerosis (MS). The objectives are to determine the relationships among psychiatric (e.g., depression or anxiety) and vascular (e.g., diabetes, hypertension, etc.) comorbidities, cognitive performance, and MRI measures of brain structure and function, including changes over time. Because neuroimaging forms the basis for several investigations of specific neural correlates that will be reported in future publications, the goal of the current manuscript is to briefly review the CCOMS study design and baseline characteristics for participants enrolled in the three study cohorts (MS, psychiatric control, and healthy control), and provide a detailed description of the MRI hardware, neuroimaging acquisition parameters, and image processing pipelines for the volumetric, microstructural, functional, and perfusion MRI data.

Altered dynamic spontaneous neural activity in minimal hepatic encephalopathy

Background and aims:

Abnormal regional neural activity has been identified by the analysis of the static amplitude of low-frequency fluctuation (ALFF) in the setting of minimal hepatic encephalopathy (MHE). Brain activity is highly dynamic. This work sought to evaluate the temporal variability of ALFF to reveal MHE-related alterations in the dynamics of spontaneous neural activity.

Methods

A total of 29 healthy controls and 49 patients with cirrhosis [including 20 patients with MHE and 29 patients without MHE (NHE)] who underwent resting-state functional magnetic resonance imaging and Psychometric Hepatic Encephalopathy Score (PHES) examination were enrolled in this investigation. Utilizing a sliding-window approach, we calculated the dynamic ALFF (dALFF) variability to reflect the temporal dynamics of regional neural activity. An analysis of the correlation between dALFF variability and PHES was performed, and receiver operating characteristic (ROC) curve analysis to determine the potential of the dALFF variability index in identifying MHE was completed.

Results

The dALFF variability in the bilateral precuneus/posterior cingulate gyrus and left middle frontal gyrus progressively decreased from NHE to MHE group. In cirrhotic patients, the value of dALFF variability in the bilateral precuneus/posterior cingulate gyrus was positively correlated with their neurocognitive performance (r = 0.383 and P = 0.007). The index of dALFF variability in the bilateral precuneus/posterior cingulate gyrus could be used to distinguish NHE and MHE patients, with moderate power (area under the ROC curve = 0.712 and P = 0.012).

Conclusion

Our findings highlight the existence of aberrant dynamic brain function in MHE, which could underlie the neural basis of cognitive impairments and could be associated with the development of the disease. Analyzing dALFF could facilitate new biomarker identification for MHE.

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.

Decreased brain functional connectivity associated with cognitive dysfunction in women with second pregnancy

Purpose

Previous research has found that women with second pregnancy may have an increased risk of cognitive dysfunction. This study aims to investigate the intrinsic functional connectivity (FC) pattern of the DMN anchored on posterior cingulate cortex (PCC) in postpartum women, especially the parous women using resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

Twenty parous women, 26 primiparous women, and 30 nulliparous women were included for rs-fMRI scan. They were age and education well matched. A seed based FC method was conducted to reveal FC patterns with other brain regions using a region of interest in the PCC. The relationships between FC patterns and cognitive performance were further detected.

Results

Relative to primiparous women, parous women had significantly decreased FC primarily between the PCC and the right middle frontal gyrus and right parahippocampal gyrus. The decreased FC to the right parahippocampal gyrus in parous women was positively associated with the reduced DST scores (rho = 0.524, p = 0.031). Moreover, parous women compared with nulliparous women showed significantly decreased FC between the PCC and the left superior frontal gyrus and left middle frontal gyrus. The reduced FC to the left superior frontal gyrus in parous women was also positively associated with the lower DST scores (rho = 0.550, p = 0.022).

Conclusion

Our result highlights that women with second pregnancy revealed decreased FC between the DMN regions with the parahippocampal gyrus and prefrontal cortex, which was correlated with specific impaired cognitive function. This study may provide new insights into the neuropathological mechanisms of postpartum cognitive impairment and enhance our understanding of the neurobiological aspects during postpartum period.

Chemotherapy-induced functional brain abnormality in colorectal cancer patients: a resting‐state functional magnetic resonance imaging study

Introduction

Chemotherapy-induced cognitive impairment (i.e., “chemobrain”) is a common neurotoxic side-effect experienced by many cancer survivors who undergone chemotherapy. However, the central mechanism underlying chemotherapy-related cognitive impairment is still unclear. The purpose of this study was to investigate the changes of intrinsic brain activity and their associations with cognitive impairment in colorectal cancer (CRC) patients after chemotherapy.

Methods

Resting‐state functional magnetic resonance imaging data of 29 CRC patients following chemotherapy and 29 matched healthy controls (HCs) were collected in this study, as well as cognitive test data including Mini Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA) and Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). The measure of fractional amplitude of low-frequency fluctuation (fALFF) was calculated and compared between groups. The correlations between the fALFF of impaired brain region and cognitive performance were also analyzed.

Results

Compared with HCs, CRC patients following chemotherapy showed decreased fALFF values in the left anterior cingulate gyrus (ACG) and middle frontal gyrus, as well as increased fALFF values in the left superior frontal gyrus (orbital part) and middle occipital gyrus. Moreover, positive associations were identified between fALFF values of the left ACG and the total scores of MMSE, MoCA and FACT-Cog in the patient group.

Conclusion

These findings indicated that CRC patients after chemotherapy had decreased intrinsic brain activity in the left ACG, which might be vulnerable to the neurotoxic side-effect of chemotherapeutic drugs and related to chemotherapy-induced cognitive impairment.

Somatosensory Deficits After Stroke: Insights From MRI Studies

Somatosensory deficits after stroke are a major health problem, which can impair patients' health status and quality of life. With the developments in human brain mapping techniques, particularly magnetic resonance imaging (MRI), many studies have applied those techniques to unravel neural substrates linked to apoplexy sequelae. Multi-parametric MRI is a vital method for the measurement of stroke and has been applied to diagnose stroke severity, predict outcome and visualize changes in activation patterns during stroke recovery. However, relatively little is known about the somatosensory deficits after stroke and their recovery. This review aims to highlight the utility and importance of MRI techniques in the field of somatosensory deficits and synthesizes corresponding articles to elucidate the mechanisms underlying the occurrence and recovery of somatosensory symptoms. Here, we start by reviewing the anatomic and functional features of the somatosensory system. And then, we provide a discussion of MRI techniques and analysis methods. Meanwhile, we present the application of those techniques and methods in clinical studies, focusing on recent research advances and the potential for clinical translation. Finally, we identify some limitations and open questions of current imaging studies that need to be addressed in future research.

Multiview Feature Learning With Multiatlas-Based Functional Connectivity Networks for MCI Diagnosis

Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of brain regions, and these regions are traditionally parcellated with a particular brain atlas. Most existing studies have adopted a predefined brain atlas for all subjects. However, the constructed FC networks inevitably ignore the potentially important subject-specific information, particularly, the subject-specific brain parcellation. Similar to the drawback of the "single view" (versus the "multiview" learning) in medical image-based classification, FC networks constructed based on a single atlas may not be sufficient to reveal the underlying complicated differences between normal controls and disease-affected patients due to the potential bias from that particular atlas. In this study, we propose a multiview feature learning method with multiatlas-based FC networks to improve MCI diagnosis. Specifically, a three-step transformation is implemented to generate multiple individually specified atlases from the standard automated anatomical labeling template, from which a set of atlas exemplars is selected. Multiple FC networks are constructed based on these preselected atlas exemplars, providing multiple views of the FC network-based feature representations for each subject. We then devise a multitask learning algorithm for joint feature selection from the constructed multiple FC networks. The selected features are jointly fed into a support vector machine classifier for multiatlas-based MCI diagnosis. Extensive experimental comparisons are carried out between the proposed method and other competing approaches, including the traditional single-atlas-based method. The results indicate that our method significantly improves the MCI classification, demonstrating its promise in the brain connectome-based individualized diagnosis of brain diseases.

Functional connectivity in the central executive network predicts changes in binge drinking behavior during emerging adulthood: an observational prospective study

BACKGROUND AND AIMS

Binge drinking contributes to the immense public health burden associated with alcohol use, especially among younger drinkers. Little is known about the underlying neurobiology of changes in this behavior over time. This preliminary study aimed to identify neurobiological markers of binge drinking behavior change during emerging adulthood.

DESIGN

Observational prospective investigation of neurobiological predictors of binge drinking behavior.

SETTING

Communities surrounding a large, public university in the northeastern United States.

PARTICIPANTS

A total of 42 emerging adults (48% female), approximately half meeting criteria for an alcohol use disorder.

MEASUREMENTS

Past month binge drinking, the dependent variable, was assessed at two time-points (T1, T2) via self-report. Ten indices of resting-state functional connectivity within the central executive network (CEN), a brain network involved in executive function, were collected at T1 and specified as independent variables in cross-sectional and prospective Poisson models. All models controlled for age, sex, and alcohol use disorder status.

FINDINGS

The cross-sectional model yielded five significant associations between CEN connectivity and binge drinking incidence. Connections anchored primarily in the anterior CEN exhibited negative associations with binge drinking incidence (P = 0.001, 0.004, 0.011), and connections stemming from the right posterior parietal cortex exhibited positive associations with binge drinking incidence (P = 0.041, 0.045). In prospective models, stronger frontoparietal connectivity between the right dorsolateral prefrontal cortex and left posterior parietal cortex predicted greater increases in binge drinking incidence over time (P = 0.003).

CONCLUSIONS

There is an association between central executive network connectivity and heavy drinking, as well as evidence that functional pathways within the central executive network may contribute to changes in problematic drinking behaviors.

Deep Metric Representation Learning for Clinical Resting State fMRI

With growing size of resting state fMRI datasets and advances in deep learning methods, there are ever increasing opportunities to leverage progress in deep learning to solve challenging tasks in neuroimaging. In this work, we build upon recent advances in deep metric learning, to learn embeddings of rs-fMRI data, which can then be potentially used for several downstream tasks. We propose an efficient training method for our model and compare our method with other widely used models. Our experimental results indicate that deep metric learning can be used as an additional refinement step to learn representations of fMRI data, that significantly improves performance on downstream modeling tasks.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Psychedelic resting-state neuroimaging: A review and perspective on balancing replication and novel analyses

Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.

Congenital Adrenal Hyperplasia and Brain Health: A Systematic Review of Structural, Functional, and Diffusion Magnetic Resonance Imaging (MRI) Investigations

BACKGROUND

Congenital adrenal hyperplasia (CAH) is a group of genetic disorders that affects the adrenal glands and is the most common cause of primary adrenal insufficiency in children. In the past few decades, magnetic resonance imaging (MRI) has been implemented to investigate how the brain may be affected by CAH. A systematic review was conducted to evaluate and synthesize the reported evidence of brain findings related to CAH using structural, functional, and diffusion-weighted MRI.

METHODS

We searched bibliographical databases through July 2021 for brain MRI studies in individuals with CAH.

RESULTS

Twenty-eight studies were identified, including 13 case reports or series, 10 studies that recruited and studied CAH patients vs unaffected controls, and 5 studies without a matched control group. Eleven studies used structural MRI to identify structural abnormalities or quantify brain volumes, whereas 3 studies implemented functional MRI to investigate brain activity, and 3 reported diffusion MRI findings to assess white matter microstructure. Some commonly reported findings across studies included cortical atrophy and differences in gray matter volumes, as well as white matter hyperintensities, altered white matter microstructure, and distinct patterns of emotion and reward-related brain activity.

CONCLUSIONS

These findings suggest differences in brain structure and function in patients with CAH. Limitations of these studies highlight the need for CAH neuroimaging studies to incorporate larger sample sizes and follow best study design and MRI analytic practices, as well as clarify potential neurologic effects seen across the lifespan and in relation to clinical and behavioral CAH phenotypes.

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.

Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative

Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

Functional alterations in large-scale resting-state networks of amyotrophic lateral sclerosis: A multi-site study across Canada and the United States

Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disorder characterized by progressive degeneration of upper motor neurons and lower motor neurons, and frontotemporal regions resulting in impaired bulbar, limb, and cognitive function. Magnetic resonance imaging studies have reported cortical and subcortical brain involvement in the pathophysiology of ALS. The present study investigates the functional integrity of resting-state networks (RSNs) and their importance in ALS. Intra- and inter-network resting-state functional connectivity (Rs-FC) was examined using an independent component analysis approach in a large multi-center cohort. A total of 235 subjects (120 ALS patients; 115 healthy controls (HC) were recruited across North America through the Canadian ALS Neuroimaging Consortium (CALSNIC). Intra-network and inter-network Rs-FC was evaluated by the FSL-MELODIC and FSLNets software packages. As compared to HC, ALS patients displayed higher intra-network Rs-FC in the sensorimotor, default mode, right and left fronto-parietal, and orbitofrontal RSNs, and in previously undescribed networks including auditory, dorsal attention, basal ganglia, medial temporal, ventral streams, and cerebellum which negatively correlated with disease severity. Furthermore, ALS patients displayed higher inter-network Rs-FC between the orbitofrontal and basal ganglia RSNs which negatively correlated with cognitive impairment. In summary, in ALS there is an increase in intra- and inter-network functional connectivity of RSNs underpinning both motor and cognitive impairment. Moreover, the large multi-center CALSNIC dataset permitted the exploration of RSNs in unprecedented detail, revealing previously undescribed network involvement in ALS.

RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas

Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.

Structural and functional neuroimaging of the effects of the gut microbiome

Interactions between intestinal microbiota and the central nervous system profoundly influence brain structure and function. Over the past 15 years, intense research efforts have uncovered the significant association between gut microbial dysbiosis and neurologic, neurodegenerative, and psychiatric disorders; however, our understanding of the effect of gut microbiota on quantitative neuroimaging measures of brain microstructure and function remains limited. Many current gut microbiome studies specifically focus on discovering correlations between specific microbes and neurologic disease states that, while important, leave critical mechanistic questions unanswered. To address this significant gap in knowledge, quantitative structural and functional brain imaging has emerged as a vital bridge and as the next step in understanding how the gut microbiome influences the brain. In this review, we examine the current state-of-the-art, raise awareness of this important topic, and aim to highlight immense new opportunities-in both research and clinical imaging-for the imaging community in this emerging field of study. Our review also highlights the potential for preclinical imaging of germ-free and gnotobiotic models to significantly advance our understanding of the causal mechanisms by which the gut microbiome alters neural microstructure and function. KEY POINTS: • Alterations to the gut microbiome can significantly influence brain structure and function in health and disease. • Quantitative neuroimaging can help elucidate the effect of gut microbiota on the brain and with future translational advances, neuroimaging will be critical for both diagnostic assessment and therapeutic monitoring.

Changes in a sensorimotor network, occipital network, and psychomotor speed within three months after focal surgical injury in pediatric patients with intracranial space-occupying lesions

BACKGROUND

Studies on cognition and brain networks after various forms of brain injury mainly involve traumatic brain injury, neurological disease, tumours, and mental disease. There are few related studies on surgical injury and even fewer pediatric studies. This study aimed to preliminarily explore the cognitive and brain network changes in children with focal, unilateral, well-bounded intracranial space-occupying lesions (ISOLs) in the short term period after surgery.

METHODS

We enrolled 15 patients (6-14 years old) with ISOLs admitted to the Department of Pediatric Neurosurgery of the Beijing Tiantan Hospital between July 2020 and August 2021. Cognitive assessment and resting-state functional magnetic resonance imaging (rs-fMRI) were performed. Regional homogeneity (Reho), seed-based analysis (SBA) and graph theory analysis (GTA) were performed. Paired T-test was used for statistical analysis of cognitive assessment and rs-fMRI. Gaussian random-field theory correction (voxel p-value < 0.001, cluster p-value < 0.05) was used for Reho and SBA. False discovery rate correction (corrected p value < 0.05) for GTA.

RESULTS

Our results showed that psychomotor speed decreased within three months after surgery. Further, rs-fMRI data analysis suggested that sensorimotor and occipital network activation decreased with low information transmission efficiency.

CONCLUSION

We prudently concluded that the changes in cognitive function and brain network within three months after surgery may be similar to ageing and that the brain is vulnerable during this period.

Ventral Attention Network Correlates With High Traits of Emotion Dysregulation in Community Women - A Resting-State EEG Study

In recent years, many studies have focused on resting-state brain activity, and especially on functional connectivity (FC), an approach that typically describes the statistical interdependence of activity in distant brain regions through specific networks. Our aim was to study the neurophysiological correlates of emotion dysregulation. Therefore, we expected that both the Default Mode Network (DMN), and the Ventral Attention Network (VAN) would have been involved. Indeed, the latter plays a role in the automatic orienting of attention towards biologically salient stimuli and includes key regions for emotion control and modulation. Starting from a community sample of 422 female students, we selected 25 women with high traits of emotion dysregulation (HD group) and 25 with low traits (LD group). They underwent a 64-channel EEG recording during a five-minute resting state with eyes open. Seed-based FC was computed on the EEG Alpha band (8-13 Hz) as a control band, and on EEG Gamma power (30-50 Hz) as the relevant measure. The power within each network and inter-network connectivity (Inter-NC) was also calculated. Analysis of the EEG Gamma band revealed, in the HD group, higher levels of Inter-NC between the VAN and all other resting-state networks as compared with the LD group, while no differences emerged in the Alpha band. Concerning correlations, Alpha power in the VAN was negatively correlated in the HD group with affective lability (ALS-18 questionnaire), both for total score (ρ = -0.52, p FDR < 0.01) and the Depression/Elation subscale) ρ = -0.45, p FDR < 0.05). Consistent with this, in the Gamma band, a positive correlation was found between VAN spectral power and the Depression/Elation subscale of ALS-18, again in the HD group only (ρ = 0.47, p FDR < 0.05). In conclusion, both resting state FC and network power in the VAN were found to be related to high emotion dysregulation, even in our non-clinical sample with high traits. Emotion dysregulation was characterized, in the EEG gamma band, by a VAN strongly connected to all other networks, a result that points, in women prone to emotion dysregulation, to a strong automatic orienting of attention towards their internal state, bodily sensations, and emotionally intense related thoughts.

Altered Brain Function in First-Episode and Recurrent Depression: A Resting-State Functional Magnetic Resonance Imaging Study

Background

Studies on differences in brain function activity between the first depressive episode (FDE) and recurrent depressive episodes (RDE) are scarce. In this study, we used regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF) as indices of abnormal brain function activity. We aimed to determine the differences in these indices between patients with FDE and those with RDE, and to investigate the correlation between areas of abnormal brain function and clinical symptoms.

Methods

A total of 29 patients with RDE, 28 patients with FDE, and 29 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging were included in this study. The ReHo and ALFF measurements were used for image analysis and further analysis of the correlation between different brain regions and clinical symptoms.

Results

Analysis of variance showed significant differences among the three groups in ReHo and ALFF in the frontal, parietal, temporal, and occipital lobes. ReHo was higher in the right inferior frontal triangular gyrus and lower in the left inferior temporal gyrus in the RDE group than in the FDE group. Meanwhile, ALFF was higher in the right inferior frontal triangular gyrus, left anterior cingulate gyrus, orbital part of the left middle frontal gyrus, orbital part of the left superior frontal gyrus, and right angular gyrus, but was lower in the right lingual gyrus in the RDE group than in the FDE group. ReHo and ALFF were lower in the left angular gyrus in the RDE and FDE groups than in the HC group. Pearson correlation analysis showed a positive correlation between the ReHo and ALFF values in these abnormal areas in the frontal lobe and the severity of depressive symptoms (P < 0.05). Abnormal areas in the temporal and occipital lobes were negatively correlated with the severity of depressive symptoms (P < 0.05).

Conclusion

The RDE and FDE groups had abnormal neural function activity in some of the same brain regions. ReHo and ALFF were more widely distributed in different brain regions and had more complex neuropathological mechanisms in the RDE group than in the FDE group, especially in the right inferior frontal triangular gyrus of the frontal lobe.

Multimodal Presurgical Evaluation of Medically Refractory Focal Epilepsy in Adults: An Update for Radiologists

Surgery is a potentially curative treatment option for patients with medically refractory focal epilepsy. Advanced neuroimaging modalities often improve surgical outcomes by contributing key information during the highly individualized surgical planning process and intraoperative localization. Hence, neuroradiologists play an integral role as part of the multidisciplinary management team. In this review, we initially present the conceptual background and practical framework of the presurgical evaluation process, including a description of the surgical treatment approaches in medically refractory focal epilepsy in adults. This background is followed by an overview of the advanced modalities commonly used during the presurgical workup at level IV epilepsy centers including diffusion imaging techniques, blood oxygen level dependent (BOLD) functional MRI (fMRI), PET, SPECT, and subtraction ictal SPECT, as well as by introductions to 7-T MRI and electrophysiologic techniques including electroencephalography (EEG) and magnetoencephalography (MEG). We also provide illustrative case examples of multimodal neuroimaging including PET/MRI, PET/MRI-DTI, subtraction ictal SPECT, and image-guided stereotactic planning with fMRI-DTI.

Probing responses to deep brain stimulation with functional magnetic resonance imaging

BACKGROUND

Deep brain stimulation (DBS) is an established treatment for certain movement disorders and has additionally shown promise for various psychiatric, cognitive, and seizure disorders. However, the mechanisms through which stimulation exerts therapeutic effects are incompletely understood. A technique that may help to address this knowledge gap is functional magnetic resonance imaging (fMRI). This is a non-invasive imaging tool which permits the observation of DBS effects in vivo.

OBJECTIVE

The objective of this review was to provide a comprehensive overview of studies in which fMRI during active DBS was performed, including studied disorders, stimulated brain regions, experimental designs, and the insights gleaned from stimulation-evoked fMRI responses.

METHODS

We conducted a systematic review of published human studies in which fMRI was performed during active stimulation in DBS patients. The search was conducted using PubMED and MEDLINE.

RESULTS

The rate of fMRI DBS studies is increasing over time, with 37 studies identified overall. The median number of DBS patients per study was 10 (range = 1-67, interquartile range = 11). Studies examined fMRI responses in various disease cohorts, including Parkinson's disease (24 studies), essential tremor (3 studies), epilepsy (3 studies), obsessive-compulsive disorder (2 studies), pain (2 studies), Tourette syndrome (1 study), major depressive disorder, anorexia, and bipolar disorder (1 study), and dementia with Lewy bodies (1 study). The most commonly stimulated brain region was the subthalamic nucleus (24 studies). Studies showed that DBS modulates large-scale brain networks, and that stimulation-evoked fMRI responses are related to the site of stimulation, stimulation parameters, patient characteristics, and therapeutic outcomes. Finally, a number of studies proposed fMRI-based biomarkers for DBS treatment, highlighting ways in which fMRI could be used to confirm circuit engagement and refine DBS therapy.

CONCLUSION

A review of the literature reflects an exciting and expanding field, showing that the combination of DBS and fMRI represents a uniquely powerful tool for simultaneously manipulating and observing neural circuitry. Future work should focus on relatively understudied disease cohorts and stimulated regions, while focusing on the prospective validation of putative fMRI-based biomarkers.

Altered resting state functional connectivity in youth with congenital heart disease operated during infancy

Congenital heart disease (CHD) has been associated with structural brain growth and long-term developmental impairments, including deficits in learning, memory, and executive functions. Altered functional connectivity has been shown to be altered in neonates born with CHD; however, it is unclear if these early life alterations are also present during adulthood. Therefore, this study aimed to compare resting state functional connectivity networks associated with executive function deficits between youth (16 to 24 years old) with complex CHD (mean age = 20.13; SD = 2.35) who underwent open-heart surgery during infancy and age- and sex-matched controls (mean age = 20.41; SD = 2.05). Using the Behavior Rating Inventory of Executive Function–Adult Version questionnaire, we found that participants with CHD presented with poorer performance on the inhibit, initiate, emotional control, working memory, self-monitor, and organization of materials clinical scales than healthy controls. We then compared the resting state networks theoretically corresponding to these impaired functions, namely the default mode, dorsal attention, fronto-parietal, fronto-orbital, and amygdalar networks, between the two groups. Participants with CHD presented with decreased functional connectivity between the fronto-orbital cortex and the hippocampal regions and between the amygdala and the frontal pole. Increased functional connectivity was observed within the default mode network, the dorsal attention network, and the fronto-parietal network. Overall, our results suggest that youth with CHD present with disrupted resting state functional connectivity in widespread networks and regions associated with altered executive functioning.

Improving Depressive Symptoms of Post-stroke Depression Using the Shugan Jieyu Capsule: A Resting-State Functional Magnetic Resonance Imaging Study

Regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) were used to detect the neuroimaging mechanism of Shugan Jieyu Capsule (SG) in ameliorating depression of post-stroke depression (PSD) patients. Fifteen PSD patients took SG for 8 weeks, completed the 24-item Hamilton Depression Scale (HAMD) assessment at the baseline and 8 weeks later, and underwent functional magnetic resonance imaging (fMRI) scanning. Twenty-one healthy controls (HCs) underwent these assessments at the baseline. We found that SG improved depression of PSD patients, in which ReHo values decreased in the left calcarine sulcus (CAL.L) and increased in the left superior frontal gyrus (SFG.L) of PSD patients at the baseline. The fALFF values of the left inferior parietal cortex (IPL.L) decreased in PSD patients at the baseline. Abnormal functional activities in the brain regions were reversed to normal levels after the administration of SG for 8 weeks. Receiver operating characteristic (ROC) analysis found that the changes in three altered brain regions could be used to differentiate PSD patients at the baseline and HCs. Average signal values of altered regions were related to depression in all subjects at the baseline. Our results suggest that SG may ameliorate depression of PSD patients by affecting brain region activity and local synchronization.

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

Resting-state fMRI functional connectivity and mindfulness in clinical and non-clinical contexts: A review and synthesis

This review synthesizes relations between mindfulness and resting-state fMRI functional connectivity of brain networks. Mindfulness is characterized by present-moment awareness and experiential acceptance, and relies on attention control, self-awareness, and emotion regulation. We integrate studies of functional connectivity and (1) trait mindfulness and (2) mindfulness meditation interventions. Mindfulness is related to functional connectivity in the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Specifically, mindfulness-mediated functional connectivity changes include (1) increased connectivity between posterior cingulate cortex (DMN) and dorsolateral prefrontal cortex (FPN), which may relate to attention control; (2) decreased connectivity between cuneus and SN, which may relate to self-awareness; (3) increased connectivity between rostral anterior cingulate cortex region and dorsomedial prefrontal cortex (DMN) and decreased connectivity between rostral anterior cingulate cortex region and amygdala region, both of which may relate to emotion regulation; and lastly, (4) increased connectivity between dorsal anterior cingulate cortex (SN) and anterior insula (SN) which may relate to pain relief. While further study of mindfulness is needed, neural signatures of mindfulness are emerging.