Why is Clinical fMRI in a Resting State?

Stationarity assessment of resting state condition via permutation entropy on EEG recordings

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is the default condition to characterize brain dynamics. Its successful implementation relies both on the capacity of subjects to comply with the requirement of staying awake while not performing any cognitive task, and on the capacity of the experimenter to validate that compliance. Here we propose a novel approach, based on permutation entropy, to assess the reliability of the resting state hypothesis by evaluating its stability during a recording. We combine the calculation of permutation entropy with a method to estimate its uncertainty out of a single time series. The approach is showcased on electroencephalographic data recorded from young and elderly subjects and considering eyes-closed and eyes-opened resting state conditions. Besides highlighting the reliability of the approach, the results show higher instability in elderly subjects, hinting at qualitative differences between age groups in the distribution of unstable brain activity. The method can be applied to other kinds of electrophysiological data, like magnetoencephalographic recordings. In addition, provided that suitable hardware and software processing units are used, the implementation of the method can be translated into a real time one.

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

An inclusive multivariate approach to neural localization of language components

To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex.

Task-based functional neuroimaging in infants: a systematic review

Background

Infancy is characterized by rapid neurological transformations leading to consolidation of lifelong function capabilities. Studying the infant brain is crucial for understanding how these mechanisms develop during this sensitive period. We review the neuroimaging modalities used with infants in stimulus-induced activity paradigms specifically, for the unique opportunity the latter provide for assessment of brain function.

Methods

Conducted a systematic review of literature published between 1977-2021, via a comprehensive search of four major databases. Standardized appraisal tools and inclusion/exclusion criteria were set according to the PRISMA guidelines.

Results

Two-hundred and thirteen papers met the criteria of the review process. The results show clear evidence of overall cumulative growth in the number of infant functional neuroimaging studies, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to be the most utilized and fastest growing modalities with behaving infants. However, there is a high level of exclusion rates associated with technical limitations, leading to limited motor control studies (about 6 % ) in this population.

Conclusion

Although the use of functional neuroimaging modalities with infants increases, there are impediments to effective adoption of existing technologies with this population. Developing new imaging modalities and experimental designs to monitor brain activity in awake and behaving infants is vital.

3T vs. 7T fMRI: capturing early human memory consolidation after motor task utilizing the observed higher functional specificity of 7T

Objective

Functional magnetic resonance imaging (fMRI) visualizes brain structures at increasingly higher resolution and better signal-to-noise ratio (SNR) as field strength increases. Yet, mapping the blood oxygen level dependent (BOLD) response to distinct neuronal processes continues to be challenging. Here, we investigated the characteristics of 7 T-fMRI compared to 3 T-fMRI in the human brain beyond the effect of increased SNR and verified the benefits of 7 T-fMRI in the detection of tiny, highly specific modulations of functional connectivity in the resting state following a motor task.

Methods

18 healthy volunteers underwent two resting state and a stimulus driven measurement using a finger tapping motor task at 3 and 7 T, respectively. The SNR for each field strength was adjusted by targeted voxel size variation to minimize the effect of SNR on the field strength specific outcome. Spatial and temporal characteristics of resting state ICA, network graphs, and motor task related activated areas were compared. Finally, a graph theoretical approach was used to detect resting state modulation subsequent to a simple motor task.

Results

Spatial extensions of resting state ICA and motor task related activated areas were consistent between field strengths, but temporal characteristics varied, indicating that 7 T achieved a higher functional specificity of the BOLD response than 3 T-fMRI. Following the motor task, only 7 T-fMRI enabled the detection of highly specific connectivity modulations representing an "offline replay" of previous motor activation. Modulated connections of the motor cortex were directly linked to brain regions associated with memory consolidation.

Conclusion

These findings reveal how memory processing is initiated even after simple motor tasks, and that it begins earlier than previously shown. Thus, the superior capability of 7 T-fMRI to detect subtle functional dynamics promises to improve diagnostics and therapeutic assessment of neurological diseases.

Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data

OBJECTIVE

This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.

METHODS

Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.

RESULTS

fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10-4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.

CONCLUSION

LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

Implementation of Automated Pipeline for Resting-State fMRI Analysis with PACS Integration

In recent years, the quantity and complexity of medical imaging acquisition and processing have increased tremendously. The explosion in volume and need for advanced imaging analysis have led to the creation of numerous software programs, which have begun to be incorporated into clinical practice for indications such as automated stroke assessment, brain tumor perfusion processing, and hippocampal volume analysis. Despite these advances, there remains a need for specialized, custom-built software for advanced algorithms and new areas of research that is not widely available or adequately integrated in these "out-of-the-box" solutions. The purpose of this paper is to describe the implementation of an image-processing pipeline that is versatile and simple to create, which allows for rapid prototyping of image analysis algorithms and subsequent testing in a clinical environment. This pipeline uses a combination of Orthanc server, custom MATLAB code, and publicly available FMRIB Software Library and RestNeuMap tools to automatically receive and analyze resting-state functional MRI data collected from a custom filter on the MR scanner output. The processed files are then sent directly to Picture Archiving and Communications System (PACS) without the need for user input. This initial experience can serve as a framework for those interested in simple implementation of an automated pipeline customized to clinical needs.

Abnormal cortical-striatal-thalamic-cortical circuit centered on the thalamus in MDD patients with somatic symptoms: Evidence from the REST-meta-MDD project

OBJECTIVE

Somatic symptoms are common comorbidities of major depressive disorder (MDD), and negatively impact the course and severity of the disease. In order to enrich the understanding of the pathological mechanism and clarify the neurobiological basis of somatic symptoms in depression, we attempted to explore the changes of brain structure and function in a large sample between depression with and without somatic symptoms.

METHODS

Structure magnetic resonance imaging (MRI) data were collected from 342 patients with somatic symptoms (SD), 208 patients without somatic symptoms (NSD), and 510 healthy controls (HCs) based on the REST-meta-MDD project. We analyzed the whole brain VBM maps of the three groups, and combined with weight degree centrality (DC) index, we investigated whether the brain regions with gray matter volume (GMV) and gray matter density (GMD) abnormalities in MDD patients with somatic symptoms had corresponding brain functional abnormalities.

RESULTS

Between depression with and without somatic symptoms, we found that there are extensive GMV and GMD differences involving cortical regions such as the temporal lobe, occipital lobe, and insula, as well as subcortical brain regions such as thalamus and striatum. The comparison results of weight DC signals of GMV and GMD abnormal clusters between the SD and NSD groups were basically consistent with the GMV and GMD abnormal clusters.

CONCLUSION

The results indicate that the structure and function of cortical-striatal-thalamic-cortical (CSTC) circuit centered on the thalamus were abnormal in MDD patients with somatic symptoms. This may be the neurobiological basis of somatic symptoms in MDD.

Advances in neuroimaging studies of alcohol use disorder (AUD)

Alcohol use disorder (AUD) is a worldwide problem and the most common substance use disorder. Chronic alcohol consumption may have negative effects on the body, the mind, the family, and even society. With the progress of current neuroimaging methods, an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis, prognosis, and treatment assessment of AUD. This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods, structural magnetic resonance imaging, functional magnetic resonance imaging, and electroencephalography, as well as the most common noninvasive brain stimulation - transcranial magnetic stimulation, and intersperses the article with joint intra- and intergroup studies, providing an outlook on future research directions.

Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type

To improve 'bench-to-bedside' translation, it is integral that knowledge flows bidirectionally-from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data-an emerging mode applicable in animal models-as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

Can this data be saved? Techniques for high motion in resting state scans of first grade children

Motion remains a significant technical hurdle in fMRI studies of young children. Our aim was to develop a straightforward and effective method for obtaining and preprocessing resting state data from a high-motion pediatric cohort. This approach combines real-time monitoring of head motion with a preprocessing pipeline that uses volume censoring and concatenation alongside independent component analysis based denoising. We evaluated this method using a sample of 108 first grade children (age 6-8) enrolled in a longitudinal study of math development. Data quality was assessed by analyzing the correlation between participant head motion and two key metrics for resting state data, temporal signal-to-noise and functional connectivity. These correlations should be minimal in the absence of noise-related artifacts. We compared these data quality indicators using several censoring thresholds to determine the necessary degree of censoring. Volume censoring was highly effective at removing motion-corrupted volumes and ICA denoising removed much of the remaining motion artifact. With the censoring threshold set to exclude volumes that exceeded a framewise displacement of 0.3 mm, preprocessed data met rigorous standards for data quality while retaining a large majority of subjects (83 % of participants). Overall, results show it is possible to obtain usable resting-state data despite extreme motion in a group of young, untrained subjects.

Exploring the relationship between resting state intra-network connectivity and accelerometer-measured physical activity in pediatric concussion: A cohort study

Our objective was to explore the association between resting state functional connectivity and accelerometer-measured physical activity in pediatric concussion. Fourteen children with concussion (aged 14.54 ± 2.39 years, 8 female) were included in this secondary data analysis of a larger study. Participants had neuroimaging at 15.3 ± 6.7 days post-injury and subsequently a mean of 11.1 ± 5.0 days of accelerometer data. Intra-network connectivity of the default mode network (DMN), sensorimotor network (SMN), salience network (SN), and fronto-parietal network (FPN) was computed using resting state functional MRI. We found that per general linear models, only intra-network connectivity of the DMN was associated with physical activity levels. More specifically, increased intra-network connectivity of the DMN was significantly associated with higher levels of subsequent accelerometer-measured light physical activity (F(2, 11) = 7.053, p = 0.011, Ra2 = 0.562; β = 0.469), moderate physical activity (F(2, 11) = 7.053, p = 0.011, Ra2 = 0.562; β = 0.725), and vigorous physical activity (F(2, 11) = 10.855, p = 0.002, Ra2 = 0.664; β = 0.79). Intra-network connectivity of the DMN did not significantly predict sedentary time. Therefore, these preliminary findings suggest that there is a positive association between the intra-network connectivity of the DMN and device-measured physical activity in children with concussion.

Effects of Different Scan Duration on Brain Effective Connectivity among Default Mode Network Nodes

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) can evaluate brain functional connectivity without requiring subjects to perform a specific task. This rs-fMRI is very useful in patients with cognitive decline or unable to respond to tasks. However, long scan durations have been suggested to measure connectivity between brain areas to produce more reliable results, which are not clinically optimal. Therefore, this study aims to evaluate a shorter scan duration and compare the scan duration of 10 and 15 min using the rs-fMRI approach. Methods: Twenty-one healthy male and female participants (seventeen right-handed and four left-handed), with ages ranging between 21 and 60 years, were recruited. All participants underwent both 10 and 15 min of rs-fMRI scans. The present study evaluated the default mode network (DMN) areas for both scan durations. The areas involved were the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), left inferior parietal cortex (LIPC), and right inferior parietal cortex (RIPC). Fifteen causal models were constructed and inverted using spectral dynamic causal modelling (spDCM). The models were compared using Bayesian Model Selection (BMS) for group studies. Result: The BMS results indicated that the fully connected model was the winning model among 15 competing models for both 10 and 15 min scan durations. However, there was no significant difference in effective connectivity among the regions of interest between the 10 and 15 min scans. Conclusion: Scan duration in the range of 10 to 15 min is sufficient to evaluate the effective connectivity within the DMN region. In frail subjects, a shorter scan duration is more favourable.

Altered Resting Brain Functions in Patients With Irritable Bowel Syndrome: A Systematic Review

Background

The neural activity of irritable bowel syndrome (IBS) patients in the resting state without any intervention has not been systematically studied. The purpose of this study was to compare the resting-state brain functions of IBS patients with healthy controls (HCs).

Methods

The published neuroimage studies were obtained from electronic databases including PubMed, EMBASE, PsycINFO, Web of Science Core, CNKI Database, Wanfang Database, VIP Database, and CBMdisc. Search dates were from inception to March 14th, 2022. The studies were identified by the preidentified inclusion and exclusion criteria. Two independent reviewers compiled the studies and evaluated them for quality and bias.

Results

Altogether 22 fMRI studies were included in this review. The risk of bias of the included studies was generally low. The findings indicated that in IBS patients, increased or decreased brain areas were mostly associated with visceral sensations, emotional processing, and pain processing. According to brain network research, IBS may exhibit anomalies in the DMN, CEN, and emotional arousal networks. The fluctuations in emotion (anxiety, sadness) and symptoms in IBS patients were associated with alterations in the relevant brain regions.

Conclusion

This study draws a preliminary conclusion that there are insufficient data to accurately distinguish the different neurological features of IBS in the resting state. Additional high-quality research undertaken by diverse geographic regions and teams is required to reach reliable results regarding resting-state changed brain regions in IBS.

MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy

Background

At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency.

Methods

20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi.

Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age.

Results

Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05).

Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age.

Conclusions

Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Selective Impairment of Long-Range Default Mode Network Functional Connectivity as a Biomarker for Preclinical Alzheimer's Disease in People with Down Syndrome

BACKGROUND

Down syndrome (DS) is associated with increased risk for Alzheimer's disease (AD). In neurotypical individuals, clinical AD is preceded by reduced resting state functional connectivity in the default mode network (DMN), but it is unknown whether changes in DMN connectivity predict clinical onset of AD in DS.

OBJECTIVE

Does lower DMN functional connectivity predict clinical onset of AD and cognitive decline in people with DS?

METHODS

Resting state functional MRI (rsfMRI), longitudinal neuropsychological, and clinical assessment data were collected on 15 nondemented people with DS (mean age = 51.66 years, SD = 5.34 years, range = 42-59 years) over four years, during which 4 transitioned to dementia. Amyloid-β (Aβ) PET data were acquired on 13 of the 15 participants. Resting state fMRI, neuropsychological, and clinical assessment data were also acquired on an independent, slightly younger unimpaired sample of 14 nondemented people with DS (mean age = 44.63 years, SD = 7.99 years, range = 38-61 years).

RESULTS

Lower functional connectivity between long-range but not short-range DMN regions predicts AD diagnosis and cognitive decline in people with DS. Aβ accumulation in the inferior parietal cortex is associated with lower regional DMN functional connectivity.

CONCLUSION

Reduction of long-range DMN connectivity is a potential biomarker for AD in people with DS that precedes and predicts clinical conversion.

Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis

Symptomatic overlap of depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) is a major diagnostic and therapeutic problem. Mania in medical history remains the only reliable distinguishing marker which is problematic given that episodes of depression compared to episodes of mania are more frequent and predominantly present at the beginning of BD. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive, task-free, and well-tolerated method that may provide diagnostic markers acquired from spontaneous neural activity. Previous rs-fMRI studies focused on differentiating BD from MDD depression were inconsistent in their findings due to low sample power, heterogeneity of compared samples, and diversity of analytical methods. This meta-analysis investigated resting-state activity differences in BD and MDD depression using activation likelihood estimation. PubMed, Web of Science, Scopus and Google Scholar databases were searched for whole-brain rs-fMRI studies which compared MDD and BD currently depressed patients between Jan 2000 and August 2020. Ten studies were included, representing 234 BD and 296 MDD patients. The meta-analysis found increased activity in the left insula and adjacent area in MDD compared to BD. The finding suggests that the insula is involved in neural activity patterns during resting-state that can be potentially used as a biomarker differentiating both disorders.

Aggression subtypes relate to distinct resting state functional connectivity in children and adolescents with disruptive behavior

There is increasing evidence for altered brain resting state functional connectivity in adolescents with disruptive behavior. While a considerable body of behavioral research points to differences between reactive and proactive aggression, it remains unknown whether these two subtypes have dissociable effects on connectivity. Additionally, callous-unemotional traits are important specifiers in subtyping aggressive behavior along the affective dimension. Accordingly, we examined associations between two aggression subtypes along with callous-unemotional traits using a seed-to-voxel approach. Six functionally relevant seeds were selected to probe the salience and the default mode network, based on their presumed role in aggression. The resting state sequence was acquired from 207 children and adolescents of both sexes [mean age (standard deviation) = 13.30 (2.60); range = 8.02-18.35] as part of a Europe-based multi-center study. One hundred eighteen individuals exhibiting disruptive behavior (conduct disorder/oppositional defiant disorder) with varying comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms were studied, together with 89 healthy controls. Proactive aggression was associated with increased left amygdala-precuneus coupling, while reactive aggression related to hyper-connectivities of the posterior cingulate cortex (PCC) to the parahippocampus, the left amygdala to the precuneus and to hypo-connectivity between the right anterior insula and the nucleus caudate. Callous-unemotional traits were linked to distinct hyper-connectivities to frontal, parietal, and cingulate areas. Additionally, compared to controls, cases demonstrated reduced connectivity of the PCC and left anterior insula to left frontal areas, the latter only when controlling for ADHD scores. Taken together, this study revealed aggression-subtype-specific patterns involving areas associated with emotion, empathy, morality, and cognitive control.

A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas "touches" the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

Reviewing applications of structural and functional MRI for bipolar disorder

Bipolar disorders (BDs) represent one of the leading causes of disability and morbidity globally. The use of functional magnetic resonance imaging (fMRI) is being increasingly studied as a tool to improve the diagnosis and treatment of BDs. While morphological biomarkers can be identified through the use of structural magnetic resonance imaging (sMRI), recent studies have demonstrated that varying degrees of both structural and functional impairments indicate differing bipolar subtypes. Within fMRI, resting-state fMRI has specifically drawn increased interest for its capability to detect different neuronal activation patterns compared to task-based fMRI. This study aims to review recently published literature regarding the use of fMRI to investigate structural-functional relationships in BD diagnosis and specifically resting-state fMRI to provide an opinion on fMRI's modern clinical application. All sources in this literature review were collected through searches on both PubMed and Google Scholar databases for terms such as 'resting-state fMRI' and 'functional neuroimaging biomarkers of bipolar disorder'. While there are promising results supporting the use of fMRI for improving differential accuracy and establishing clinically relevant biomarkers, additional evidence will be required before fMRI is considered a dependable component of the overall BD diagnostic process.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

Intrasubject functional connectivity related to self-generated thoughts

INTRODUCTION

In psychiatric research, functional connectivity (FC) derived from resting-state functional MRI (rsfMRI) is often used to investigate brain abnormalities in psychiatric disorders. This approach assumes implicitly that FC can recover reliable maps of the functional architecture of the brain and that these profiles of connectivity reflect trait differences underlying pathology. However, evidence of FC related to self-generated thoughts (mind-wandering) stands in contrast with these assumptions, as FC may reflect thought patterns rather than functional architecture.

METHODS

Multi-factor analysis (MFA) was used to investigate the reported content of self-generated thoughts during high-field (7T) rsfMRI in a repeated sample of 22 healthy individuals. To investigate the relationship between these experiences and FC, individual scores for each of these dimensions were compared with whole-brain connectivity using the network-based statistic (NBS) method.

RESULTS

This analysis revealed three dimensions of thought content: self-referential thought, negative thoughts about one's surroundings, and thoughts in the form of imagery. A network of connections within the sensorimotor cortices negatively correlated with self-generated thoughts concerning the self was observed (p = .0081, .0486 FDR).

CONCLUSION

These results suggest a potentially confounding relationship between self-generated thoughts and FC, and contribute to the body of research concerning the functional representation of mind-wandering.

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

Applications of Functional Magnetic Resonance Imaging in Determining the Pathophysiological Mechanisms and Rehabilitation of Spatial Neglect

Functional magnetic resonance imaging (fMRI) is a neuroimaging tool which has been applied extensively to explore the pathophysiological mechanisms of neurological disorders. Spatial neglect is considered to be the failure to attend or respond to stimuli on the side of the space or body opposite a cerebral lesion. In this review, we summarize and analyze fMRI studies focused specifically on spatial neglect. Evidence from fMRI studies have highlighted the role of dorsal and ventral attention networks in the pathophysiological mechanisms of spatial neglect, and also support the concept of interhemispheric rivalry as an explanatory model. fMRI studies have shown that several rehabilitation methods can induce activity changes in brain regions implicated in the control of spatial attention. Future investigations with large study cohorts and appropriate subgroup analyses should be conducted to confirm the possibility that fMRI might offer an objective standard for predicting spatial neglect and tracking the response of brain activity to clinical treatment, as well as provide biomarkers to guide rehabilitation for patients with SN.

Ground-truth "resting-state" signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T₂* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four "best-case" scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6-18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8-19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40-70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T₂* decay, and S₀-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations.

Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.

Aberrant Awake Spontaneous Brain Activity in Obstructive Sleep Apnea: A Review Focused on Resting-State EEG and Resting-State fMRI

As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.

Functional Connectivity Signatures of Parkinson's Disease

 Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson’s disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks re-organization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.

Current Challenges in Translational and Clinical fMRI and Future Directions

Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.

Is treated HIV infection still toxic to the brain?

Clinically apparent HIV infection, accompanied by CNS opportunistic infections and HIV encephalopathy, was often associated with profound structural and functional brain effects prior to the introduction of anti-retroviral therapy (ART). With treatment, HIV structural and functional brain effects are smaller and have not been as easily detected. With near complete elimination of CNS opportunistic infections, the HIV neuroimaging research community now grapples with the problem of detecting subtler structural and functional changes against a background of persisting confounds, such as comorbidities and clinical features common in the HIV infected population. This situation also raises the question of whether imaging measure changes that are reported as HIV brain effects are purely related to viral infection, rather than originating from confounding effects that might include age, substance use, hepatitis C coinfection, cerebrovascular risk factors, ART, premorbid cognitive skills and illness duration. In addition to cohort characteristics, variation in image acquisition and analysis techniques may also contribute to study outcome heterogeneity. We review the potential effects of these confounds on detection of HIV infection effects and discuss strategies to avoid or mitigate the effects of these confounds. We then present a systematic approach to measurement, design and analysis in HIV neuroimaging studies, combining both experimental and statistical control techniques to determine if HIV infection effects persist, fluctuate or worsen in groups achieving viral suppression from ART.