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

Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury

OBJECTIVES

This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI).

METHODS

Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers.

RESULTS

Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%).

CONCLUSIONS

A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.

MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers

With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.

Comparative analysis of brain language templates with primary language areas detected from presurgical fMRI of brain tumor patients

INTRODUCTION

Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients.

METHODS

Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated.

RESULTS

For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D.

CONCLUSION

This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies.

The Prediction of Brain Activity from Connectivity: Advances and Applications

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review

Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.

Different brain functional network abnormalities between attention-deficit/hyperactivity disorder youth with and without familial risk for bipolar disorder

Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone.

The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study

The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.

Resting-state functional connectivity in children cooled for neonatal encephalopathy

Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.

Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.

Contributions of the left and right thalami to language: A meta-analytic approach

BACKGROUND

Despite a pervasive cortico-centric view in cognitive neuroscience, subcortical structures including the thalamus have been shown to be increasingly involved in higher cognitive functions. Previous structural and functional imaging studies demonstrated cortico-thalamo-cortical loops which may support various cognitive functions including language. However, large-scale functional connectivity of the thalamus during language tasks has not been examined before.

METHODS

The present study employed meta-analytic connectivity modeling to identify language-related coactivation patterns of the left and right thalami. The left and right thalami were used as regions of interest to search the BrainMap functional database for neuroimaging experiments with healthy participants reporting language-related activations in each region of interest. Activation likelihood estimation analyses were then carried out on the foci extracted from the identified studies to estimate functional convergence for each thalamus. A functional decoding analysis based on the same database was conducted to characterize thalamic contributions to different language functions.

RESULTS

The results revealed bilateral frontotemporal and bilateral subcortical (basal ganglia) coactivation patterns for both the left and right thalami, and also right cerebellar coactivations for the left thalamus, during language processing. In light of previous empirical studies and theoretical frameworks, the present connectivity and functional decoding findings suggest that cortico-subcortical-cerebellar-cortical loops modulate and fine-tune information transfer within the bilateral frontotemporal cortices during language processing, especially during production and semantic operations, but also other language (e.g., syntax, phonology) and cognitive operations (e.g., attention, cognitive control).

CONCLUSION

The current findings show that the language-relevant network extends beyond the classical left perisylvian cortices and spans bilateral cortical, bilateral subcortical (bilateral thalamus, bilateral basal ganglia) and right cerebellar regions.

Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors, and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence.

COVID-19 neuroimaging update: pathophysiology, acute findings, and post-acute developments

COVID-19 has prominent effects on the nervous system with important manifestations on neuroimaging. In this review, we discuss the neuroimaging appearance of acute COVID-19 that became evident during the early stages of the pandemic. We highlight the underlying pathophysiology mediating nervous system effects and neuroimaging appearances including systemic inflammatory response such as cytokine storm, coagulopathy, and para/post-infections immune mediated phenomena. We also discuss the nervous system manifestations of COVID-19 and the role of imaging as the pandemic has evolved over time, including related to the development of vaccines and the emergence of post-acute sequalae such as long COVID.

Reduced dorsal fronto-striatal connectivity at rest in anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a serious psychiatric illness that remains difficult to treat. Elucidating the neural mechanisms of AN is necessary to identify novel treatment targets and improve outcomes. A growing body of literature points to a role for dorsal fronto-striatal circuitry in the pathophysiology of AN, with increasing evidence of abnormal task-based fMRI activation within this network among patients with AN. Whether these abnormalities are present at rest and reflect fundamental differences in brain organization is unclear.

METHODS

The current study combined resting-state fMRI data from patients with AN (n = 89) and healthy controls (HC; n = 92) across four studies, removing site effects using ComBat harmonization. First, the a priori hypothesis that dorsal fronto-striatal connectivity strength - specifically between the anterior caudate and dlPFC - differed between patients and HC was tested using seed-based functional connectivity analysis with small-volume correction. To assess specificity of effects, exploratory analyses examined anterior caudate whole-brain connectivity, amplitude of low-frequency fluctuations (ALFF), and node centrality.

RESULTS

Compared to HC, patients showed significantly reduced right, but not left, anterior caudate-dlPFC connectivity (p = 0.002) in small-volume corrected analyses. Whole-brain analyses also identified reduced connectivity between the right anterior caudate and left superior frontal and middle frontal gyri (p = 0.028) and increased connectivity between the right anterior caudate and right occipital cortex (p = 0.038). No group differences were found in analyses of anterior caudate ALFF and node centrality.

CONCLUSIONS

Decreased coupling of dorsal fronto-striatal regions indicates that circuit-based abnormalities persist at rest and suggests this network may be a potential treatment target.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Advanced magnetic resonance neuroimaging techniques: feasibility and applications in long or post-COVID-19 syndrome - a review

Long-term or post-COVID-19 syndrome (PCS) is a condition that affects people infected with SARS‑CoV‑2, the virus that causes COVID-19. PCS is characterized by a wide range of persistent or new symptoms that last months after the initial infection, such as fatigue, shortness of breath, cognitive dysfunction, and pain. Advanced magnetic resonance (MR) neuroimaging techniques can provide valuable information on the structural and functional changes in the brain associated with PCS as well as potential biomarkers for diagnosis and prognosis. In this review, we discuss the feasibility and applications of various advanced MR neuroimaging techniques in PCS, including perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), functional MR imaging (fMRI), diffusion tensor imaging (DTI), and tractography. We summarize the current evidence on neuroimaging findings in PCS, the challenges and limitations of these techniques, and the future directions for research and clinical practice. Although still uncertain, advanced MRI techniques show promise for gaining insight into the pathophysiology and guiding the management of COVID-19 syndrome, pending larger validation studies.

Resting state functional connectome in breast cancer patients with fear of cancer recurrence

This study aimed to investigate network-level brain functional changes in breast cancer patients and their relationship with fear of cancer recurrence (FCR). Resting-state functional MRI was collected from 43 patients with breast cancer and 40 healthy controls (HCs). Graph theory analyses, whole-brain voxel-wise functional connectivity strength (FCS) analyses and seed-based functional connectivity (FC) analyses were performed to identify connection alterations in breast cancer patients. Correlations between brain functional connections (i.e. FCS and FC) and FCR level were assessed to further reveal the neural mechanisms of FCR in breast cancer patients. Graph theory analyses indicated a decreased clustering coefficient in breast cancer patients compared to HCs (P = 0.04). Patients with breast cancer exhibited significantly higher FCS in both higher-order function networks (frontoparietal, default mode, and dorsal attention systems) and primary somatomotor networks. Among the hyperconnected regions in breast cancer, the left inferior frontal operculum demonstrated a significant positive correlation with FCR. Our findings suggest that breast cancer patients exhibit less segregation of brain function, and the left inferior frontal operculum is a key region associated with FCR. This study offers insights into the neural mechanisms of FCR in breast cancer patients at the level of brain connectome.

Structural and functional changes following brain surgery in pediatric patients with intracranial space-occupying lesions

We explored the structural and functional changes of the healthy hemisphere of the brain after surgery in children with intracranial space-occupying lesions. We enrolled 32 patients with unilateral intracranial space-occupying lesions for brain imaging and cognitive assessment. Voxel-based morphometry and surface-based morphometry analyses were used to investigate the structural images of the healthy hemisphere. Functional images were analyzed using regional homogeneity, amplitude of low-frequency fluctuations, and fractional-amplitude of low-frequency fluctuations. Voxel-based morphometry and surface-based morphometry analysis used the statistical model built into the CAT 12 toolbox. Paired t-tests were used for functional image and cognitive test scores. For structural image analysis, we used family-wise error correction of peak level (p < 0.05), and for functional image analysis, we use Gaussian random-field theory correction (voxel p < 0.001, cluster p < 0.05). We found an increase in gray matter volume in the healthy hemisphere within six months postoperatively, mainly in the frontal lobe. Regional homogeneity and fractional-amplitude of low-frequency fluctuations also showed greater functional activity in the frontal lobe. The results of cognitive tests showed that psychomotor speed and motor speed decreased significantly after surgery, and reasoning increased significantly after surgery. We concluded that in children with intracranial space-occupying lesions, the healthy hemisphere exhibits compensatory structural and functional effects within six months after surgery. This effect occurs mainly in the frontal lobe and is responsible for some higher cognitive compensation. This may provide some guidance for the rehabilitation of children after brain surgery.

Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography

Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.

Mitigation of Fetal Irradiation Injury from Mid-Gestation Total Body Radiation with Mitochondrial-Targeted GS-Nitroxide JP4-039

Victims of a radiation terrorist event will include pregnant women and unborn fetuses. Mitochondrial dysfunction and oxidative stress are key pathogenic factors of fetal irradiation injury. The goal of this preclinical study is to investigate the efficacy of mitigating fetal irradiation injury by maternal administration of the mitochondrial-targeted gramicidin S (GS)- nitroxide radiation mitigator, JP4-039. Pregnant female C57BL/6NTac mice received 3 Gy total body ionizing irradiation (TBI) at mid-gestation embryonic day 13.5 (E13.5). Using novel time- and-motion-resolved 4D in utero magnetic resonance imaging (4D-uMRI), we found TBI caused extensive injury to the fetal brain that included cerebral hemorrhage, loss of cerebral tissue, and hydrocephalus with excessive accumulation of cerebrospinal fluid (CSF). Histopathology of the fetal mouse brain showed broken cerebral vessels and elevated apoptosis. Further use of novel 4D Oxy-wavelet MRI capable of probing in vivo mitochondrial function in intact brain revealed significant reduction of mitochondrial function in the fetal brain after 3Gy TBI. This was validated by ex vivo Oroboros mitochondrial respirometry. Maternal administration JP4-039 one day after TBI (E14.5), which can pass through the placental barrier, significantly reduced fetal brain radiation injury and improved fetal brain mitochondrial respiration. This also preserved cerebral brain tissue integrity and reduced cerebral hemorrhage and cell death. As JP4-039 administration did not change litter sizes or fetus viability, together these findings indicate JP4-039 can be deployed as a safe and effective mitigator of fetal radiation injury from mid-gestational in utero ionizing radiation exposure.

One Sentence Summary

Mitochondrial-targeted gramicidin S (GS)-nitroxide JP4-039 is safe and effective radiation mitigator for mid-gestational fetal irradiation injury.

Intrinsic brain functional connectivity predicts treatment-related motor complications in early Parkinson's disease patients

BACKGROUND

Treatment-related motor complications may develop progressively over the course of Parkinson's disease (PD).

OBJECTIVE

We investigated intrinsic brain networks functional connectivity (FC) at baseline in a cohort of early PD patients which successively developed treatment-related motor complications over 4 years.

METHODS

Baseline MRI images of 88 drug-naïve PD patients and 20 healthy controls were analyzed. After the baseline assessments, all PD patients were prescribed with dopaminergic treatment and yearly clinically re-assessed. At the 4-year follow-up, 36 patients have developed treatment-related motor complications (PD-Compl) whereas 52 had not (PD-no-Compl). Single-subject and group-level independent component analyses were used to investigate FC changes within the major large-scale resting-state networks at baseline. A multivariate Cox regression model was used to explore baseline predictors of treatment-related motor complications at 4-year follow-up.

RESULTS

At baseline, an increased FC in the right middle frontal gyrus within the frontoparietal network as well as a decreased connectivity in the left cuneus within the default-mode network were detected in PD-Compl compared with PD-no-Compl. PD-Compl patients showed a preserved sensorimotor FC compared to controls. FC differences were found to be independent predictors of treatment-related motor complications over time.

CONCLUSION

Our findings demonstrated that specific FC differences may characterize drug-naïve PD patients more prone to develop treatment-related complications. These findings may reflect the presence of an intrinsic vulnerability across frontal and prefrontal circuits, which may be potentially targeted as a future biomarker in clinical trials.

Cognitive Motor Dissociation: Gap Analysis and Future Directions

BACKGROUND

Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance.

METHODS

The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs.

RESULTS

The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces.

CONCLUSIONS

To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.

A multimodal meta-analysis of regional functional and structural brain abnormalities in obsessive-compulsive disorder

Numerous neuroimaging studies of resting-state functional imaging and voxel-based morphometry (VBM) have revealed abnormalities in specific brain regions in obsessive-compulsive disorder (OCD), but results have been inconsistent. We conducted a whole-brain voxel-wise meta-analysis on resting-state functional imaging and VBM studies that investigated differences of functional activity and gray matter volume (GMV) between patients with OCD and healthy controls (HCs) using seed-based d mapping (SDM) software. A total of 41 independent studies (51 datasets) for resting-state functional imaging and 42 studies (46 datasets) for VBM were included by a systematic literature search. Overall, patients with OCD displayed increased spontaneous functional activity in the bilateral inferior frontal gyrus (IFG) (extending to the bilateral insula) and bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), as well as decreased spontaneous functional activity in the bilateral paracentral lobule, bilateral cerebellum, left caudate nucleus, left inferior parietal gyri, and right precuneus cortex. For the VBM meta-analysis, patients with OCD displayed increased GMV in the bilateral thalamus (extending to the bilateral cerebellum), right striatum, and decreased GMV in the bilateral mPFC/ACC and left IFG (extending to the left insula). The conjunction analyses found that the bilateral mPFC/ACC, left IFG (extending to the left insula) showed decreased GMV with increased intrinsic function in OCD patients compared to HCs. This meta-analysis demonstrated that OCD exhibits abnormalities in both function and structure in the bilateral mPFC/ACC, insula, and IFG. A few regions exhibited only functional or only structural abnormalities in OCD, such as the default mode network, striatum, sensorimotor areas, and cerebellum. It may provide useful insights for understanding the underlying pathophysiology of OCD and developing more targeted and efficacious treatment and intervention strategies.

Morphological and functional brain changes in chronic cancer-related pain: A systematic review

The purpose of this study was to perform a systematic review of the available literature on morphological and functional brain changes measured by modern neuroimaging techniques in patients suffering from chronic cancer-related pain. A systematic search was conducted in PubMed, Embase, and Web of Science using different keyword combinations. In addition, a hand search was performed on the reference lists and several databases to retrieve supplementary primary studies. Eligible articles were assessed for methodological quality and risk of bias and reviewed by two independent researchers. The search yielded only four studies, three of which used MRI and one PET-CT. None of the studies measured longitudinal morphological (i.e., gray or white matter) changes. All studies investigated functional brain changes and found differences in specific brain regions and networks between patients with chronic cancer-related pain and pain-free cancer patients or healthy volunteers. Some of these alterations were found in brain networks that also show changes in non-cancer populations with chronic pain (e.g., the default mode network and salience network). However, specific findings were inconsistent, and there was substantial variation in imaging methodology, analysis, sample size, and study quality. There is a striking lack of research on morphological brain changes in patients with chronic cancer-related pain. Moreover, only a few studies investigated functional brain changes. In the retrieved studies, there is some evidence that alterations occur in brain networks also involved in other chronic non-cancer pain syndromes. However, the low sample sizes of the studies, finding inconsistencies, and methodological heterogeneity do not allow for robust conclusions.

Effects of glabellar botulinum toxin injections on resting-state functional connectivity in borderline personality disorder

Meta-analyses suggest a sustained alleviation of depressive symptoms through glabellar botulinum toxin (BTX) injections. This can be explained by the disruption of facial feedback loops, which may moderate and reinforce the experience of negative emotions. Borderline personality disorder (BPD) is characterized by excessive negative emotions. Here, a seed-based resting-state functional connectivity (rsFC) analysis following BTX (N = 24) or acupuncture (ACU, N = 21) treatment in BPD is presented on areas related to the motor system and emotion processing. RsFC in BPD using a seed-based approach was analyzed. MRI data were measured before and 4 weeks after treatment. Based on previous research, the rsFC focus was on limbic and motor areas as well as the salience and default mode network. Clinically, after 4 weeks both groups showed a reduction of borderline symptoms. However, the anterior cingulate cortex (ACC) and the face area in the primary motor cortex (M1) displayed aberrant rsFC after BTX compared to ACU treatment. The M1 showed higher rsFC to the ACC after BTX treatment compared to ACU treatment. In addition, the ACC displayed an increased connectivity to the M1 as well as a decrease to the right cerebellum. This study shows first evidence for BTX-specific effects in the motor face region and the ACC. The observed effects of BTX on rsFC to areas are related to motor behavior. Since symptom improvement did not differ between the two groups, a BTX-specific effect seems plausible rather than a general therapeutic effect.

Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data

Background

The World Health Organization has reported that approximately 300 million individuals suffer from the mood disorder known as MDD. Non-invasive measurement techniques have been utilized to reveal the mechanism of MDD, with rsfMRI being the predominant method. The previous functional connectivity and energy landscape studies have shown the difference in the coactivation patterns between MDD and HCs. However, these studies did not consider oscillatory temporal dynamics.

Methods

In this study, the dynamic mode decomposition, a method to compute a set of coherent spatial patterns associated with the oscillation frequency and temporal decay rate, was employed to investigate the alteration of the occurrence of dynamic modes between MDD and HCs. Specifically, The BOLD signals of each subject were transformed into dynamic modes representing coherent spatial patterns and discrete-time eigenvalues to capture temporal variations using dynamic mode decomposition. All the dynamic modes were disentangled into a two-dimensional manifold using t-SNE. Density estimation and density ratio estimation were applied to the two-dimensional manifolds after the two-dimensional manifold was split based on HCs and MDD.

Results

The dynamic modes that uniquely emerged in the MDD were not observed. Instead, we have found some dynamic modes that have shown increased or reduced occurrence in MDD compared with HCs. The reduced dynamic modes were associated with the visual and saliency networks while the increased dynamic modes were associated with the default mode and sensory-motor networks.

Conclusion

To the best of our knowledge, this study showed initial evidence of the alteration of occurrence of the dynamic modes between MDD and HCs. To deepen understanding of how the alteration of the dynamic modes emerges from the structure, it is vital to investigate the relationship between the dynamic modes, cortical thickness, and surface areas.

Efficacy of Cerebellar Transcranial Magnetic Stimulation for Post-stroke Balance and Limb Motor Function Impairments: Meta-analyses of Random Controlled Trials and Resting-State fMRI Studies

This study aimed to investigate the potential therapeutic effects of cerebellar transcranial magnetic stimulation (TMS) on balance and limb motor impairments in stroke patients. A meta-analysis of randomized controlled trials was conducted to assess the effects of cerebellar TMS on balance and motor impairments in stroke patients. Additionally, an activation likelihood estimation (ALE) meta-analysis was performed on resting-state functional magnetic resonance imaging (fMRI) studies to compare spontaneous neural activity differences between stroke patients and healthy controls using measures including the amplitude of low frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo). The analysis included 10 cerebellar TMS studies and 18 fMRI studies. Cerebellar TMS treatment demonstrated significant improvements in the Berg Balance Scale score (p < 0.0001) and the Fugl-Meyer Assessment lower extremity score (p < 0.0001) compared to the control group in stroke patients. Additionally, spontaneous neural activity alterations were identified in motor-related regions after stroke, including the precentral gyrus, putamen, thalamus, and paracentral lobule. Cerebellar TMS shows promise as a therapeutic intervention to enhance balance and lower limb motor function in stroke patients. It is easy for clinical application and addresses the limitations of insufficient direct stimulation depth on the leg area of the cortex. However, further research combining neuroimaging outcomes with clinical measurements is necessary to validate these findings.

Recent developments and future avenues for human corticospinal neuroimaging

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Analyzing 20 years of Resting-State fMRI Research: Trends and collaborative networks revealed

Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Trajectories and contributing factors of neural compensation in healthy and pathological aging

Neural degeneration is a hallmark of healthy aging and can be associated with specific cognitive impairments. However, neural degeneration per se is not matched by unremitting declines in cognitive abilities. Instead, middle-aged and older adults typically maintain surprisingly high levels of cognitive functioning, suggesting that the human brain can adapt to structural degeneration by neural compensation. Here, we summarize prevailing theories and recent empirical studies on neural compensation with a focus on often neglected contributing factors, such as lifestyle, metabolism and neural plasticity. We suggest that these factors moderate the relationship between structural integrity and neural compensation, maintaining psychological well-being and behavioral functioning. Finally, we discuss that a breakdown in neural compensation may pose a tipping point that distinguishes the trajectories of healthy vs pathological aging, but conjoint support from psychology and cognitive neuroscience for this alluring view is still scarce. Therefore, future experiments that target the concomitant processes of neural compensation and associated behavior will foster a comprehensive understanding of both healthy and pathological aging.

Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns

When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.

Network-based statistics distinguish anomic and Broca's aphasia

INTRODUCTION

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM.

RESULTS

NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest.

DISCUSSION

We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features

Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.

Sleep Deficits Inter-Link Lower Basal Forebrain-Posterior Cingulate Connectivity and Perceived Stress and Anxiety Bidirectionally in Young Men

BACKGROUND

The basal nucleus of Meynert (BNM), a primary source of cholinergic projections to the cortex, plays key roles in regulating the sleep-wake cycle and attention. Sleep deficit is associated with impairment in cognitive and emotional functions. However, whether or how cholinergic circuit, sleep, and cognitive/emotional dysfunction are inter-related remains unclear.

METHODS

We curated the Human Connectome Project data and explored BNM resting state functional connectivities (rsFC) in relation to sleep deficit, based on the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective reports of emotional states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated at a corrected threshold. We assessed the correlation between BNM rsFC, PSQI, and clinical measurements with Pearson regressions and their inter-relationships with mediation analyses.

RESULTS

In whole-brain regressions with age and alcohol use severity as covariates, men showed lower BNM rsFC with the posterior cingulate cortex (PCC) in correlation with PSQI score. No clusters were identified in women at the same threshold. Both BNM-PCC rsFC and PSQI score were significantly correlated with anxiety, perceived stress, and neuroticism scores in men. Moreover, mediation analyses showed that PSQI score mediated the relationship between BNM-PCC rsFC and these measures of negative emotions bidirectionally in men.

CONCLUSIONS

Sleep deficit is associated with negative emotions and lower BNM rsFC with the PCC. Negative emotional states and BNM-PCC rsFC are bidirectionally related through poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states.

Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification

Introduction

Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps.

Methods

In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction.

Results

As the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients.

Discussion

We validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification.

Arterial occlusion duration affects the cuff-induced hyperemic response in skeletal muscle BOLD perfusion imaging as shown in young healthy subjects

OBJECTIVE

Dynamic BOLD MRI with cuff compression, inducing ischemia and post-occlusive hyperemia in skeletal muscle, has been pointed out as a potential diagnostic tool to assess peripheral limb perfusion. The objective was to explore the robustness of this technique and its sensitivity to the occlusion duration.

MATERIALS AND METHODS

BOLD images were acquired at 3 T in 14 healthy volunteers. [Formula: see text]-imaging with 5- and 1.5-min occlusions were acquired and several semi-quantitative BOLD parameters were derived from ROI-based [Formula: see text]-time curves. Differences in parameters from the two different occlusion durations were evaluated in the gastrocnemius and soleus muscles using non-parametrical tests. Intra- and inter-scan repeatability were evaluated with coefficient of variation.

RESULTS

Longer occlusion duration resulted in an increased hyperemic signal effect yielding significantly different values (p < 0.05) in gastrocnemius for all parameters describing the hyperemic response, and in soleus for two of these parameters. Specifically, 5-min occlusion yielded steeper hyperemic upslope in gastrocnemius (41.0%; p < 0.05) and soleus (59.7%; p = 0.03), shorter time to half peak in gastrocnemius (46.9%; p = 0.00008) and soleus (33.5%; p = 0.0003), and shorter time to peak in gastrocnemius (13.5%; p = 0.02). Coefficients of variation were lower than percentage differences that were found significant.

DISCUSSION

Findings show that the occlusion duration indeed influences the hyperemic response and thus should play a part in future methodological developments.

Characteristic dynamic functional connectivity during sevoflurane-induced general anesthesia

General anesthesia (GA) during surgery is commonly maintained by inhalational sevoflurane. Previous resting state functional MRI (rs-fMRI) studies have demonstrated suppressed functional connectivity (FC) of the entire brain networks, especially the default mode networks, transitioning from the awake to GA condition. However, accuracy and reliability were limited by previous administration methods (e.g. face mask) and short rs-fMRI scans. Therefore, in this study, a clinical scenario of epilepsy patients undergoing laser interstitial thermal therapy was leveraged to acquire 15 min of rs-fMRI while under general endotracheal anesthesia to maximize the accuracy of sevoflurane level. Nine recruited patients had fMRI acquired during awake and under GA, of which seven were included in both static and dynamic FC analyses. Group independent component analysis and a sliding-window method followed by k-means clustering were applied to identify four dynamic brain states, which characterized subtypes of FC patterns. Our results showed that a low-FC brain state was characteristic of the GA condition as a single featuring state during the entire rs-fMRI session; In contrast, the awake condition exhibited frequent fluctuations between three distinct brain states, one of which was a highly synchronized brain state not seen in GA. In conclusion, our study revealed remarkable dynamic connectivity changes from awake to GA condition and demonstrated the advantages of dynamic FC analysis for future studies in the assessments of the effects of GA on brain functional activities.

Resting-state fMRI in patients with refractory epilepsy with and without drop attacks: exploring the connectivity of sensorimotor cortex

OBJECTIVE

Patients with multifocal or generalized epilepsies manifesting with drop attacks have severe refractory seizures and significant cognitive and behavioural abnormalities. It is unclear to what extent these features relate to network abnormalities and how networks in sensorimotor cortex differ from those in patients with refractory focal epilepsies. Thus, in this study we sought to provide preliminary data on connectivity of sensorimotor cortex in patients with epileptic drop attacks, in comparison to patients with focal refractory epilepsies.

METHODS

Resting-state fMRI (rs-fMRI) data was available for 5 patients with epileptic drop attacks and 15 with refractory focal epilepsies undergoing presurgical evaluation. Functional connectivity was analyzed with a seed-based protocol, with primary seeds placed at the precentral gyrus, the postcentral gyrus and the premotor cortex. For each seed, the subjects' timeseries were extracted and transformed to Z scores. Between-group analysis was then performed using the 3dttest+ + AFNI program.

RESULTS

Two clusters of reduced connectivity in the group with drop attacks (DA group) in relation to those with focal epilepsies were found in the between-group analysis: the precentral seed showed reduced connectivity in the surrounding motor area, and the postcentral seed, reduced connectivity with the ipsilateral posterior cingulate gyrus. In the intra-group analyses, sensorimotor and premotor networks were abnormal in the DA group, whereas patients with focal epilepsies had the usual connectivity maps with each seed.

CONCLUSION

This pilot study shows differences in the cerebral connectivity in the sensorimotor cortex of patients with generalized epilepsies and drop attacks which should be further explored to better understand the biological bases of the seizure generation and cognitive changes in these people.

Randomised placebo-controlled clinical trial evaluating the impact of a new visual rehabilitation program on neuroadaptation in patients implanted with trifocal intraocular lenses

PURPOSE

To evaluate the efficacy of a new visual training program for improving the visual function in patients implanted with trifocal intraocular lenses (IOLs).

METHODS

Randomised placebo-controlled clinical trial enrolling 60 subjects (age, 47-75 years) undergoing cataract surgery with implantation of trifocal diffractive IOL. Home-based active visual training was prescribed immediately after surgery to all of them (20 sessions, 30 min): 31 subjects using a serious game based on Gabor patches (study group) and 29 using a placebo software (placebo group). Visual acuity, contrast sensitivity (CS), and perception of visual disturbances (QoV questionnaire) were evaluated before and after training. Likewise, in a small subgroup, resting-state functional magnetic resonance imaging (rs-fMRI) analysis was performed.

RESULTS

No significant differences were found between groups in compliance time (p = 0.70). After training, only significant improvements in monocular uncorrected intermediate visual acuity were found in the study group (p ≤ 0.01), although differences between groups did not reach statistical significance (p ≥ 0.11). Likewise, significantly better binocular far CS values were found in the study group for the spatial frequencies of 6 (p = 0.01) and 12 cpd (p = 0.03). More visual symptoms of the QoV questionnaire experienced a significant change in the level of bothersomeness in the study group. Rs-fMRI revealed the presence significant changes reflecting higher functional connectivity after the training with the serious game.

CONCLUSIONS

A 3-week visual training program based on the use of Gabor patches after bilateral implantation of trifocal diffractive IOLs may be beneficial for optimising the visual function, with neural changes associated suggesting an acceleration of neuroadaptation. Trial registration ClinicalTrials.gov, NCT04985097. Registered 02 August 2021, https://clinicaltrials.gov/(NCT04985097 ).

Characterization of Resting-State Functional Connectivity Changes in Hypertension by a Modified Difference Degree Test

Introduction: Hypertension affects over a billion people worldwide, and the application of neuroimaging may elucidate changes brought about by the disease. We have applied a graph theory approach to examine the organizational differences in resting-state functional magnetic resonance imaging (rs-fMRI) data between hypertensive and normotensive participants. To detect these groupwise differences, we performed statistical testing using a modified difference degree test (DDT). Methods: Structural and rs-fMRI data were collected from a cohort of 52 total (29 hypertensive and 23 normotensive) participants. Functional connectivity maps were obtained by partial correlation analysis of participant rs-fMRI data. We modified the DDT null generation algorithm and validated the change through different simulation schemes and then applied this modified DDT to our experimental data. Results: Through a comparative analysis, the modified DDT showed higher true positivity rates (TPR) when compared with the base DDT while also maintaining false positivity rates below the nominal value of 5% in nearly all analytically thresholded trials. Applying the modified DDT to our rs-fMRI data showed differential organization in the hypertension group in the regions throughout the brain including the default mode network. These experimental findings agree with previous studies. Conclusions: While our findings agree with previous studies, the experimental results presented require more investigation to prove their link to hypertension. Meanwhile, our modification to the DDT results in higher accuracy and an increased ability to discern groupwise differences in rs-fMRI data. We expect this to be useful in studying groupwise organizational differences in future studies.

An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study

Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within "normal" brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures-Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE-aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models.

Functional dysconnectivity in youth depression: Systematic review, meta-analysis, and network-based integration

Youth depression has been associated with heterogenous patterns of aberrant brain connectivity. To make sense of these divergent findings, we conducted a systematic review encompassing 19 resting-state fMRI seed-to-whole-brain studies (1400 participants, comprising 795 youths with major depression and 605 matched healthy controls). We incorporated separate meta-analyses of connectivity abnormalities across the levels of the most commonly seeded brain networks (default-mode and limbic networks) and, based on recent additions to the literature, an updated meta-analysis of amygdala dysconnectivity in youth depression. Our findings indicated broad and distributed findings at an anatomical level, which could not be captured by conventional meta-analyses in terms of spatial convergence. However, we were able to parse the complexity of region-to-region dysconnectivity by considering constituent regions as components of distributed canonical brain networks. This integration revealed dysconnectivity centred on central executive, default mode, salience, and limbic networks, converging with findings from the adult depression literature and suggesting similar neurobiological underpinnings of youth and adult depression.

Sleep dysfunction mediates the relationship between hypothalamic-insula connectivity and anxiety-depression symptom severity bidirectionally in young adults

BACKGROUND

The hypothalamus plays a crucial role in regulating sleep-wake cycle and motivated behavior. Sleep disturbance is associated with impairment in cognitive and affective functions. However, how hypothalamic dysfunction may contribute to inter-related sleep, cognitive, and emotional deficits remain unclear.

METHODS

We curated the Human Connectome Project dataset and investigated how hypothalamic resting state functional connectivities (rsFC) were associated with sleep dysfunction, as evaluated by the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective mood states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated with a corrected threshold. We examined the inter-relationship amongst hypothalamic rsFC, PSQI score, and clinical measures with mediation analyses.

RESULTS

In whole-brain regressions with age and drinking severity as covariates, men showed higher hypothalamic rsFC with the right insula in correlation with PSQI score. No clusters were identified in women at the same threshold. Both hypothalamic-insula rsFC and PSQI score were significantly correlated with anxiety and depression scores in men. Further, mediation analyses showed that PSQI score mediated the relationship between hypothalamic-insula rsFC and anxiety/depression symptom severity bidirectionally in men.

CONCLUSIONS

Sleep dysfunction is associated with negative emotions and hypothalamic rsFC with the right insula, a core structure of the interoceptive circuits. Notably, anxiety-depression symptom severity and altered hypothalamic-insula rsFC are related bidirectionally by poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states that need to be further investigated.

Drooling disrupts the brain functional connectivity network in Parkinson's disease

AIMS

This study aimed to investigate the causal interaction between significant sensorimotor network (SMN) regions and other brain regions in Parkinson's disease patients with drooling (droolers).

METHODS

Twenty-one droolers, 22 PD patients without drooling (non-droolers), and 22 matched healthy controls underwent 3T-MRI resting-state scans. We performed independent component analysis and Granger causality analysis to determine whether significant SMN regions help predict other brain areas. Pearson's correlation was computed between imaging characteristics and clinical characteristics. ROC curves were plotted to assess the diagnostic performance of effective connectivity (EC).

RESULTS

Compared with non-droolers and healthy controls, droolers showed abnormal EC of the right caudate nucleus (CAU.R) and right postcentral gyrus to extensive brain regions. In droolers, increased EC from the CAU.R to the right middle temporal gyrus was positively correlated with MDS-UPDRS, MDS-UPDRS II, NMSS, and HAMD scores; increased EC from the right inferior parietal lobe to CAU.R was positively correlated with MDS-UPDRS score. ROC curve analysis showed that these abnormal ECs are of great significance in diagnosing drooling in PD.

CONCLUSION

This study identified that PD patients with drooling have abnormal EC in the cortico-limbic-striatal-cerebellar and cortio-cortical networks, which could be potential biomarkers for drooling in PD.

Modeling venous bias in resting state functional MRI metrics

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set

Background

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.

Method

Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.

Result

The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds.

Conclusion

This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

Functional MRI in Neuro-Oncology: State of the Art and Future Directions

Since its discovery in the early 1990s, functional MRI (fMRI) has been used to study human brain function. One well-established application of fMRI in the clinical setting is the neurosurgical planning of patients with brain tumors near eloquent cortical areas. Clinical fMRI aims to preoperatively identify eloquent cortices that serve essential functions in daily life, such as hand movement and language. The primary goal of neurosurgery is to maximize tumor resection while sparing eloquent cortices adjacent to the tumor. When a lesion presents in the vicinity of an eloquent cortex, surgeons may use fMRI to plan their best surgical approach by determining the proximity of the lesion to regions of activation, providing guidance for awake brain surgery and intraoperative brain mapping. The acquisition of fMRI requires patient preparation prior to imaging, determination of functional paradigms, monitoring of patient performance, and both processing and analysis of images. Interpretation of fMRI maps requires a strong understanding of functional neuroanatomy and familiarity with the technical limitations frequently present in brain tumor imaging, including neurovascular uncoupling, patient compliance, and data analysis. This review discusses clinical fMRI in neuro-oncology, relevant ongoing research topics, and prospective future developments in this exciting discipline.