Current methods and new directions in resting state fMRI

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BACKGROUND

Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD.

METHODS

Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability.

RESULTS

Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01).

CONCLUSIONS

Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.

Prediction of individual brain age using movie and resting-state fMRI

Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87 years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.

Altered functional activity of the precuneus and superior temporal gyrus in patients with residual dizziness caused by benign paroxysmal positional vertigo

Objective

Benign paroxysmal positional vertigo (BPPV) is a common clinical vertigo disease, and the most effective treatment for this disease is canal repositioning procedures (CRP). Most patients return to normal after a single treatment. However, some patients still experience residual dizziness (RD) after treatment, and this disease's pathogenesis is currently unclear. The purpose of this study is to explore whether there are abnormal brain functional activities in patients with RD by using resting-state functional magnetic resonance imaging (rs-fMRI) and to provide imaging evidence for the study of the pathogenesis of RD.

Materials and methods

The BPPV patients in the Second Affiliated Hospital of Xuzhou Medical University had been included from December 2021 to November 2022. All patients had been received the collection of demographic and clinical characteristics (age, gender, involved semicircular canal, affected side, CRP times, BPPV course, duration of RD symptoms, and whether they had hypertension, diabetes, coronary heart disease.), scale assessment, including Dizziness Handicap Inventory (DHI), Hamilton Anxiety Inventory (HAMA), Hamilton Depression Inventory (HAMD), rs-fMRI data collection, CRP treatment, and then a one-month follow-up. According to the follow-up results, 18 patients with RD were included. At the same time, we selected 19 healthy individuals from our hospital's physical examination center who matched their age, gender as health controls (HC). First, the amplitude of low-frequency fluctuations (ALFF) analysis method was used to compare the local functional activities of the two groups of subjects. Then, the brain regions with different ALFF results were extracted as seed points. Functional connectivity (FC) analysis method based on seed points was used to explore the whole brain FC of patients with RD. Finally, a correlation analysis between clinical features and rs-fMRI data was performed.

Results

Compared to the HC, patients with RD showed lower ALFF value in the right precuneus and higher ALFF value in the right superior temporal gyrus (STG). When using the right STG as a seed point, it was found that the FC between the right STG, the right supramarginal gyrus (SMG), and the left precuneus was decreased in RD patients. However, no significant abnormalities in the FC were observed when using the right precuneus as a seed point.

Conclusion

In patients with RD, the local functional activity of the right precuneus is weakened, and the local functional activity of the right STG is enhanced. Furthermore, the FC between the right STG, the right SMG, and the left precuneus is weakened. These changes may explain the symptoms of dizziness, floating sensation, walking instability, neck tightness, and other symptoms in patients with RD to a certain extent.

Alterations of regional spontaneous brain activity in obsessive-compulsive disorders: A meta-analysis

BACKGROUND

Recent studies using resting-state functional magnetic resonance imaging (rs-fMRI) demonstrate that there is aberrant regional spontaneous brain activity in obsessive-compulsive disorders (OCD). Nevertheless, the results of previous studies are contradictory, especially in the abnormal brain regions and the directions of their activities. It is necessary to perform a meta-analysis to identify the common pattern of altered regional spontaneous brain activity in patients with OCD.

METHODS

The present study conducted a systematic search for studies in English published up to May 2023 in PubMed, Web of Science, and Embase. These studies measured differences in regional spontaneous brain activity at the whole brain level using regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF) and the fractional amplitude of low-frequency fluctuations (fALFF). Then the Anisotropic effect-size version of seed-based d mapping (AES-SDM) was used to investigate the consistent abnormality of regional spontaneous brain activity in patients with OCD.

RESULTS

27 studies (33 datasets) were included with 1256 OCD patients (650 males, 606 females) and 1176 healthy controls (HCs) (588 males, 588 females). Compared to HCs, patients with OCD showed increased spontaneous brain activity in the right inferior parietal gyrus (Brodmann Area 39), left median cingulate and paracingulate gyri (Brodmann Area 24), bilateral inferior cerebellum, right middle frontal gyrus (Brodmann Area 46), left inferior frontal gyrus in triangular part (Brodmann Area 45) and left middle frontal gyrus in orbital part (Brodmann Area 11). Meanwhile, decreased spontaneous brain activity was identified in the right precentral gyrus (Brodmann Area 4), right insula (Brodmann Area 48), left postcentral gyrus (Brodmann Area 43), bilateral superior cerebellum and left caudate (Brodmann Area 25).

CONCLUSIONS

This meta-analysis provided a quantitative review of spontaneous brain activity in OCD. The results demonstrated that the brain regions in the frontal lobe, sensorimotor cortex, cerebellum, caudate and insula are crucially involved in the pathophysiology of OCD. This research contributes to the understanding of the pathophysiologic mechanism underlying OCD and could provide a new perspective on future diagnosis and treatment of OCD.

Harmonization of multi-site functional MRI data with dual-projection based ICA model

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings.

Aberrant age-related alterations in spontaneous cortical activity in participants with cerebral palsy

Introduction

Cerebral Palsy (CP) is the most common neurodevelopmental motor disability, resulting in life-long sensory, perception and motor impairments. Moreover, these impairments appear to drastically worsen as the population with CP transitions from adolescents to adulthood, although the underlying neurophysiological mechanisms remain poorly understood.

Methods

We began to address this knowledge gap by utilizing magnetoencephalographic (MEG) brain imaging to study how the amplitude of spontaneous cortical activity (i.e., resting state) is altered during this transition period in a cohort of 38 individuals with spastic diplegic CP (Age range = 9.80-47.50 years, 20 females) and 67 neurotypical controls (NT) (Age range = 9.08-49.40 years, Females = 27). MEG data from a five-minute eyes closed resting-state paradigm were source imaged, and the power within the delta (2-4 Hz), theta (5-7 Hz), alpha (8-12 Hz), beta (15-29 Hz), and gamma (30-59 Hz) frequency bands were computed.

Results

For both groups, the delta and theta spontaneous power decreased in the bilateral temporoparietal and superior parietal regions with age, while alpha, beta, and gamma band spontaneous power increased in temporoparietal, frontoparietal and premotor regions with age. We also found a significant group x age interaction, such that participants with CP demonstrated significantly less age-related increases in the spontaneous beta activity in the bilateral sensorimotor cortices compared to NT controls.

Discussion

Overall, these results demonstrate that the spontaneous neural activity in individuals with CP has an altered trajectory when transitioning from adolescents to adulthood. We suggest that these differences in spontaneous cortical activity may play a critical role in the aberrant motor actions seen in this patient group, and may provide a neurophysiological marker for assessing the effectiveness of current treatment strategies that are directed at improving the mobility and sensorimotor impairments seen in individuals with CP.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

Background

Brain functional connectivity under the naturalistic paradigm has been shown to be better at predicting individual behaviors than other brain states, such as rest and doing tasks. Nevertheless, the state-of-the-art methods have found it difficult to achieve desirable results from movie-watching paradigm functional magnetic resonance imaging (mfMRI) -induced brain functional connectivity, especially when there are fewer datasets. Incorporating other physical measurements into the prediction method may enhance accuracy. Eye tracking, becoming popular due to its portability and lower expense, can provide abundant behavioral features related to the output of human's cognition, and thus might supplement the mfMRI in observing participants' subconscious behaviors. However, there are very few studies on how to effectively integrate the multimodal information to strengthen the performance by a unified framework.

Objective

A fusion approach with mfMRI and eye tracking, based on convolution with edge-node switching in graph neural networks (CensNet), is proposed in this article.

Methods

In this graph model, participants are designated as nodes, mfMRI derived functional connectivity as node features, and different eye-tracking features are used to compute similarity between participants to construct heterogeneous graph edges. By taking multiple graphs as different channels, we introduce squeeze-and-excitation attention module to CensNet (A-CensNet) to integrate graph embeddings from multiple channels into one.

Results

The proposed model outperforms those using a single modality and single channel, and state-of-the-art methods.

Conclusions

The results indicate that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.

Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging

Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.

Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer's Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

Triple-network analysis of Alzheimer's disease based on the energy landscape

Introduction

Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state.

Methods

Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state.

Results

AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index.

Discussion

The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.

Synthesis and bioactivity evaluation of a myelin-specific contrast agent for magnetic resonance imaging of myelination in central nervous system

Demyelination exists in many neurological diseases of nervous system, such as stroke. Currently, magnetic resonance imaging (MRI) has been the main tool for diagnosing and monitoring the myelin related diseases. However, the conventional MRI unable to distinguish demyelinating lesions from other inflammatory lesions. To address this problem, we have designed and prepared a myelin specific magnetic resonance contrast agent, Gd-DTDAS, which was based myelin specific moiety MeDAS^g and Gd-DTPA^h. In this work, we verified the specificity and sensitivity of Gd-DTDAS to myelin. Moreover, we investigated the specific binding ability of Gd-DTDAS to myelin sheath in the MCAO miceⁱ models. The in vivo imaging results showed that Gd-DTDAS can bind to the undamaged myelin sheath in the BBB disruption areas, and in turn reduce the relaxation time. The fluorescence images also showed significant fluorescence in the brain right infarct area of the MCAO model mice with administration of Gd-DTDAS. The above results confirmed that Gd-DTDAS could be preferentially distributed in areas with high myelination and can detect focally induced demyelination.

Improving detection of fMRI activation at 1.5 T using high permittivity ceramics

In this work, we propose an application of high permittivity materials (HPMs) to improve functional magnetic resonance imaging (fMRI) at 1.5 T, increasing the receive (Rx) sensitivity of a commercial multi-channel head coil. To evaluate the transmit efficiency, specific absorption rate (SAR), and the signal-to-noise ratio (SNR) changes introduced by the HPMs with relative permittivity of 4500, we considered the following configurations in simulation: a whole-body birdcage coil and an Rx-only multi-channel head coil with and without the HPM blocks in the presence of a homogeneous head phantom or a human body model. Experimental studies were also performed with a phantom and with volunteers. Seven healthy volunteers enrolled in a prospective study of fMRI activation in the motor cortex with and without HPMs. fMRI data were analyzed using group-level paired T-tests between acquisitions with and without HPM blocks. Both electromagnetic simulations and experimental measurements showed ∼25% improvement in the Rx sensitivity of a commercial head coil in the areas of interest when HPM blocks were placed in close proximity. It increased the detected motor cortex fMRI activation volume by an average of 56%, thus resulting in more sensitive functional imaging at 1.5 T.

Applying joint graph embedding to study Alzheimer's neurodegeneration patterns in volumetric data

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer's Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Imaging synaptic density in depression

Major depressive disorder is a prevalent and heterogeneous disorder with treatment resistance in at least 50% of individuals. Most of the initial studies focused on the monoamine system; however, recently other mechanisms have come under investigation. Specific to the current issue, studies show synaptic involvement in depression. Other articles in this issue report on reductions in synaptic density, dendritic spines, boutons and glia associated with stress and depression. Importantly, it appears that some drugs (e.g., ketamine) may lead to rapid synaptic restoration or synaptogenesis. Direct evidence for this comes from preclinical work. However, neuroimaging studies, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), have become useful in assessing these changes in vivo. Here, we describe the use of neuroimaging techniques in the evaluation of synaptic alterations associated with depression in humans, as well as measurement of synaptic restoration after administration of ketamine. Although more research is desired, use of these techniques widen our understanding of depression and move us further along the path to targeted and effective treatment for depression.

A study on alterations in functional activity in migraineurs during the interictal period

Migraine is a recurrent disease in which the cumulative effect of repeated pain attacks over a long period of time causes changes in brain function. Although there are some studies focusing on the interictal period of migraine, the reproducibility of these results is poor. Therefore, we intend to use a data-driven functional connectivity (FC) approach to probe the alterations in cerebral functional activity during the interictal period, as well as underlying no-task mechanisms of inducing headache attack in migraine patients. In the current research, 24 episodic migraine patients and 23 healthy controls (HCs) were recruited. By analyzing the magnitude of regional homogeneity (ReHo) and low-frequency fractional fluctuation (fALFF), We identified alterations in spontaneous brain activity in migraineurs, including the bilateral middle frontal gyrus, left postcentral, and right lingual gyrus. Thereafter such abnormalities were selected as seeds (ROIs) for FC analysis to further explore the underlying changes between ROIs and the whole brain areas. Compared with HCs, FC between the right middle frontal gyrus with the left precuneus cortex, and bilateral thalamus were enhanced in migraineurs. In addition, increased FC has been showed between the left postcentral gyrus with the bilateral thalamus. Furthermore, negative correlation existed between fALFF values of the left middle frontal gyrus and the pain intensity of migraine attacks (r = -0.4578, p = 0.0245). In summary, abnormal FC between the bilateral thalamus and right middle frontal gyrus, or the left retrocentral gyrus may occur between attacks in migraineurs, which may be the basis for sensory integration and pain regulation dysfunction. Thus, this could become a promising biomarker for the early diagnosis and evaluation of migraine in the interictal period, and provide a novel view for further investigation of the pathogenesis and etiology of recurrent migraine.

Low Regional Homogeneity of Intrinsic Cerebellar Activity in Ankle Instability: An Externally Validated rs-fMRI Study

PURPOSE

Joint deafferentation after post-ankle sprain ligament healing can disrupt sensory input from the ankle and induce maladaptive neuroplasticity, especially in the cerebellum. This study aimed to determine whether the regional homogeneity of intrinsic cerebellar activity differs between patients with ankle instability and healthy controls without a history of ankle injury.

METHODS

The current study used a primary data set of 18 patients and 22 healthy controls and an external UK Biobank data set of 16 patients with ankle instability and 69 healthy controls for a cross-database, cross-sectional investigation. All participants underwent resting-state functional magnetic resonance imaging to calculate their regional homogeneity (ReHo) value. Between-group comparisons of the sensorimotor-related subregions of the cerebellum were first performed in the primary data set to identify low cerebellar ReHo in patients with multiple comparison corrections, and the surviving subregions were then externally validated in the UK Biobank data set. Correlation analyses between the ReHo values and clinical features were also performed.

RESULTS

The ReHo value of cerebellar lobule VIIIb was significantly lower in the ankle instability group than in the controls (0.170 ± 0.016 vs 0.184 ± 0.019 in the primary data set, 0.157 ± 0.026 vs 0.180 ± 0.042 in the UK Biobank data set). The ReHo values of this subregion showed a significant positive correlation with the Cumberland Ankle Instability Tool scores in the ankle instability group (r = 0.553, P-corrected = 0.0348).

CONCLUSIONS

Patients with ankle instability had lower intraregional coherence in cerebellar lobule VIIIb than that of controls, which was also positively correlated with the intensity of self-reported ankle instability.

Abnormal Regional Spontaneous Neural Activity and Functional Connectivity in Unmedicated Patients with Narcolepsy Type 1: A Resting-State fMRI Study

BACKGROUND

Previous Resting-state functional magnetic resonance imaging (fMRI) studies have mainly focused on cerebral functional alteration in processing different emotional stimuli in patients with narcolepsy type 1 (NT1), but were short of exploration of characteristic brain activity and its remote interaction patterns. This study aimed to investigate the spontaneous blood oxygen fluctuations at rest and to elucidate the neural mechanisms underlying neuropsychiatric behavior.

METHOD

A total of 18 unmedicated patients with NT1 and matched healthy individuals were recruited in a resting-state fMRI study. Magnetic resonance imaging (MRI) data were first analyzed using fractional low-frequency amplitude of low-frequency fluctuation (fALFF) to detect changes in local neural activity, and regions with group differences were taken as regions of interest (ROIs). Secondly, functional connectivity (FC) analysis was used to explore altered connectivity between ROIs and other areas. Lastly, the relationship between functional brain activity and neuropsychiatric behaviors was analyzed with correlation analysis.

RESULTS

fALFF analysis revealed enhanced neural activity in bilateral fusiform gyrus (FFG), right precentral gyrus, and left postcentral gyrus (PoCG) in the NT1 group. The patients indicated reduced activity in the bilateral temporal pole middle temporal gyrus (TPOmid), left caudate nucleus (CAU), left parahippocampus, left precuneus (PCUN), right amygdala, and right anterior cingulate and paracingulate gyri. ESS score was negatively correlated with fALFF in the right FFG. The NT1 group revealed decreased connectivity between left TPOmid and right PoCG, the bilateral middle frontal gyrus, left superior frontal gyrus, medial, and right supramarginal gyrus. Epworth Sleepiness Scale (ESS) was negatively correlated with FC of the left TPOmid with left putamen (PUT) in NT1. Compared with healthy controls (HCs), enhanced FC of the left CAU with right FFG was positively associated with MSLT-SOREMPs in patients. Furthermore, increased FC of the left PCUN with right PoCG was positively correlated with SDS score.

CONCLUSIONS

We found that multiple functional activities related to the processing of emotional regulation and sensory information processing were abnormal, and some were related to clinical characteristics. fALFF in the left postcentral or right precentral gyrus may be used as a biomarker of narcolepsy, whereas fALFF in the right fusiform and the FC strength of the left temporal pole middle temporal gyrus with the putamen may be clinical indicators to assess the drowsiness severity of narcolepsy.

Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder

RATIONAL

With no available response biomarkers, matching an appropriate antidepressant to an individual can be a lengthy process. Improving understanding of processes underlying treatment responsivity in depression is crucial for facilitating work on response biomarkers.

OBJECTIVES

To identify differences in patterns of pre-treatment resting-state functional connectivity (rsFC) that may underlie response to antidepressant treatment.

METHODS

After a baseline MRI scan, thirty-four drug-free patients with depression were treated with an SSRI escitalopram 10 mg daily for 6 weeks; response was defined as ≥ 50% decrease in Hamilton Depression Rating Scale (HAMD) score. Thirty-one healthy controls had a baseline clinical assessment and scan. Healthy participants did not receive treatment.

RESULTS

Twenty-one (62%) of patients responded to escitalopram. Treatment responsivity was associated with enhanced rsFC of the right fronto-parietal network (FPN)-with the posterior DMN, somatomotor network (SMN) and somatosensory association cortex. The lack of treatment response was characterized by reduced rsFC: of the bilateral FPN with the contralateral SMN, of the right FPN with the posterior DMN, and of the extended sensorimotor auditory area with the inferior parietal lobule (IPL) and posterior DMN. Reduced rsFC of the posterior DMN with IPL was seen in treatment responders, although only when compared with HC.

CONCLUSIONS

The study supports the role of resting-state networks in response to antidepressant treatment, and in particular the central role of the frontoparietal and default mode networks.

Altered spontaneous brain activity in Down syndrome and its relation with cognitive outcome

Although Down syndrome (DS) is the most common genetic cause of neurodevelopmental delay, few neuroimaging studies have explored this population. This investigation aimed to study whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences in spontaneous brain activity among young people with DS and controls and to correlate these results with cognitive outcomes. The sample comprised 18 persons with DS (age mean = 28.67, standard deviation = 4.18) and 18 controls (age mean = 28.56, standard deviation = 4.26). fALFF and ReHo analyses were performed, and the results were correlated with other cognitive variables also collected (KBIT-2 and verbal fluency test). Increased activity was found in DS using fALFF in areas involving the frontal and temporal lobes and left cerebellum anterior lobe. Decreased activity in DS was found in the left parietal and occipital lobe, the left limbic lobe and the left cerebellum posterior lobe. ReHo analysis showed increased activity in certain DS areas of the left frontal lobe and left rectus, as well as the inferior temporal lobe. The areas with decreased activity in the DS participants were regions of the frontal lobe and the right limbic lobe. Altered fALFF and ReHo were found in the DS population, and this alteration could predict the cognitive abilities of the participants. To our knowledge, this is the first study to explore regional spontaneous brain activity in a population with DS. Moreover, this study suggests the possibility of using fALFF and ReHo as biomarkers of cognitive function, which is highly important given the difficulties in cognitively evaluating this population to assess dementia. More research is needed, however, to demonstrate its utility.

Application of DTI and fMRI in moyamoya disease

Moyamoya disease (MMD) is a chronic and progressive cerebrovascular stenosis or occlusive disease that occurs near Willis blood vessels. Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) are used to detect the microstructure of white matter and the function of gray matter, respectively. The damage of these structures will lead to the change of cognitive level in patients with moyamoya disease. In this paper, the principles of DTI and fMRI, their applications and challenges in moyamoya disease are reviewed.

Inter-Network Brain Functional Connectivity in Adolescents Assigned Female at Birth Who Experience Gender Dysphoria

Gender dysphoria (GD) is characterized by distress due to an incongruence between experienced gender and sex assigned at birth. Brain functional connectivity in adolescents who experience GD may be associated with experienced gender (vs. assigned sex) and/or brain networks implicated in own-body perception. Furthermore, sexual orientation may be related to brain functional organization given commonalities in developmental mechanisms proposed to underpin GD and same-sex attractions. Here, we applied group independent component analysis to resting-state functional magnetic resonance imaging (rs-fMRI) BOLD timeseries data to estimate inter-network (i.e., between independent components) timeseries correlations, representing functional connectivity, in 17 GD adolescents assigned female at birth (AFAB) not receiving gender-affirming hormone therapy, 17 cisgender girls, and 15 cisgender boys (ages 12-17 years). Sexual orientation was represented by degree of androphilia-gynephilia and sexual attractions strength. Multivariate partial least squares analyses found that functional connectivity differed among cisgender boys, cisgender girls, and GD AFAB, with the largest difference between cisgender boys and GD AFAB. Regarding sexual orientation and age, the brain's intrinsic functional organization of GD AFAB was both similar to and different from cisgender girls, and both differed from cisgender boys. The pattern of group differences and the networks involved aligned with the hypothesis that brain functional organization is different among GD AFAB (vs. cisgender) adolescents, and certain aspects of this organization relate to brain areas implicated in own-body perception and self-referential thinking. Overall, brain functional organization of GD AFAB was generally more similar to that of cisgender girls than cisgender boys.

Connecting the dots between cell populations, whole-brain activity, and behavior

Simultaneously manipulating and monitoring both microscopic and macroscopic brain activity in vivo and identifying the linkage to behavior are powerful tools in neuroscience research. These capabilities have been realized with the recent technical advances of optogenetics and its combination with fMRI, here termed "opto-fMRI." Opto-fMRI allows for targeted brain region-, cell-type-, or projection-specific manipulation and targeted Ca 2 + activity measurement to be linked with global brain signaling and behavior. We cover the history, technical advances, applications, and important considerations of opto-fMRI in anesthetized and awake rodents and the future directions of the combined techniques in neuroscience and neuroimaging.

Estrogen, Cognitive Performance, and Functional Imaging Studies: What Are We Missing About Neuroprotection?

Menopause transition can be interpreted as a vulnerable state characterized by estrogen deficiency with detrimental systemic effects as the low-grade chronic inflammation that appears with aging and partly explains age-related disorders as cancer, diabetes mellitus and increased risk of cognitive impairment. Over the course of a lifetime, estrogen produces several beneficial effects in healthy neurological tissues as well as cardioprotective effects, and anti-inflammatory effects. However, clinical evidence on the efficacy of hormone treatment in menopausal women has failed to confirm the benefit reported in observational studies. Unambiguously, enhanced verbal memory is the most robust finding from longitudinal and cross-sectional studies, what merits consideration for future studies aiming to determine estrogen neuroprotective efficacy. Estrogen related brain activity and functional connectivity remain, however, unexplored. In this context, the resting state paradigm may provide valuable information about reproductive aging and hormonal treatment effects, and their relationship with brain imaging of functional connectivity may be key to understand and anticipate estrogen cognitive protective effects. To go in-depth into the molecular and cellular mechanisms underlying rapid-to-long lasting protective effects of estrogen, we will provide a comprehensive review of cognitive tasks used in animal studies to evaluate the effect of hormone treatment on cognitive performance and discuss about the tasks best suited to the demonstration of clinically significant differences in cognitive performance to be applied in human studies. Eventually, we will focus on studies evaluating the DMN activity and responsiveness to pharmacological stimulation in humans.

Altered spontaneous cortical activity predicts pain perception in individuals with cerebral palsy

Cerebral palsy is the most common paediatric neurological disorder and results in extensive impairment to the sensorimotor system. However, these individuals also experience increased pain perception, resulting in decreased quality of life. In the present study, we utilized magnetoencephalographic brain imaging to examine whether alterations in spontaneous neural activity predict the level of pain experienced in a cohort of 38 individuals with spastic diplegic cerebral palsy and 67 neurotypical controls. Participants completed 5 min of an eyes closed resting-state paradigm while undergoing a magnetoencephalography recording. The magnetoencephalographic data were then source imaged, and the power within the delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), low gamma (30–59 Hz) and high gamma (60–90 Hz) frequency bands were computed. The resulting power spectral density maps were analysed vertex-wise to identify differences in spontaneous activity between groups. Our findings indicated that spontaneous cortical activity was altered in the participants with cerebral palsy in the delta, alpha, beta, low gamma and high gamma bands across the occipital, frontal and secondary somatosensory cortical areas (all p_FWE < 0.05). Furthermore, we also found that the altered beta band spontaneous activity in the secondary somatosensory cortices predicted heightened pain perception in the individuals with cerebral palsy (P = 0.039). Overall, these results demonstrate that spontaneous cortical activity within individuals with cerebral palsy is altered in comparison to their neurotypical peers and may predict increased pain perception in this patient population. Potentially, changes in spontaneous resting-state activity may be utilized to measure the effectiveness of current treatment approaches that are directed at reducing the pain experienced by individuals with cerebral palsy.

Effect of Phase-Encoding Direction on Gender Differences: A Resting-State Functional Magnetic Resonance Imaging Study

Aim

Neuroimaging studies have highlighted gender differences in brain functions, but conclusions are not well established. Few studies paid attention to the influence of phase-encoding (PE) direction in echo-planar imaging on gender differences, which is a commonly used technique in functional magnetic resonance imaging (fMRI). A disadvantage of echo-planar images is the geometrical distortion and signal loss due to large susceptibility effects along the PE direction. The present research aimed to clarify how PE direction can affect the outcome of a specific research on gender differences.

Methods

We collected resting-state fMRI using anterior to posterior (AP) and posterior to anterior (PA) directions from 113 healthy participants. We calculated several commonly used indices for spontaneous brain activity including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC) of posterior cingulate cortex for each session, and performed three group comparisons: (i) AP versus PA; (ii) male versus female; (iii) interaction between gender and PE direction.

Results

The estimated indices differed substantially between the two PE directions, and the regions that exhibited differences were roughly similar for all the indices. In addition, we found that multiple brain regions showed gender differences in these estimated indices. Further, we observed an interaction effect between gender and PE direction in the bilateral middle frontal gyrus, right precentral gyrus, right postcentral gyrus, right lingual gyrus, and bilateral cerebellum posterior lobe.

Conclusion

These apparent findings revealed that PE direction can partially influence gender differences in spontaneous brain activity of resting-state fMRI. Therefore, future studies should document the adopted PE direction and appropriate selection of PE direction will be important in future resting-state fMRI studies.

Resting-State Functional MRI Metrics in Patients With Chronic Mild Traumatic Brain Injury and Their Association With Clinical Cognitive Performance

Mild traumatic brain injury (mTBI) accounts for more than 80% of people experiencing brain injuries. Symptoms of mTBI include short-term and long-term adverse clinical outcomes. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was conducted to measure voxel-based indices including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in patients suffering from chronic mTBI; 64 patients with chronic mTBI at least 3 months post injury and 40 healthy controls underwent rs-fMRI scanning. Partial correlation analysis controlling for age and gender was performed within mTBI cohort to explore the association between rs-fMRI metrics and neuropsychological scores. Compared with controls, chronic mTBI patients showed increased fALFF in the left middle occipital cortex (MOC), right middle temporal cortex (MTC), and right angular gyrus (AG), and increased ReHo in the left MOC and left posterior cingulate cortex (PCC). Enhanced FC was observed from left MOC to right precuneus; from right MTC to right superior temporal cortex (STC), right supramarginal, and left inferior parietal cortex (IPC); and from the seed located at right AG to left precuneus, left superior medial frontal cortex (SMFC), left MTC, left superior temporal cortex (STC), and left MOC. Furthermore, the correlation analysis revealed a significant correlation between neuropsychological scores and fALFF, ReHo, and seed-based FC measured from the regions with significant group differences. Our results demonstrated that alterations of low-frequency oscillations in chronic mTBI could be representative of disruption in emotional circuits, cognitive performance, and recovery in this cohort.

Gender differences in major depressive disorders: A resting state fMRI study

BACKGROUND

Major depressive disorder (MDD) has a high disability rate and brings a large disease burden to patients and the country. Significant sex differences exist in both the epidemiological and clinical features in MDD. The effect of sex on brain function in MDD is not clear now. Regional homogeneity (ReHo) and ALFF are widely used research method in the study of brain function. This research aimed to use ReHo and ALFF to explore gender differences in brain function images in MDD.

METHODS

Eighty first-episode drug-naive patients (47 women and 30 men) with MDD and 85 age, education matched healthy volunteers (47 women and 31 men) were recruited in our study and participated in resting-state functional magnetic resonance imaging scans. ReHo and ALFF were used to assess brain activity, two-way ANOVA and post hoc analysis was conducted to explore the sex difference in MDD. Correlation analysis was used to explore the relationship between abnormal brain functioning and clinical symptoms.

RESULTS

We observed sex-specific patterns and diagnostic differences in MDD Patients, further post hoc comparisons indicated that women with MDD showed decreased ALFF value in the right superior occipital gyrus and decreased ReHo value in the left calcarine and left dorsolateral superior frontal gyrus compared with HC females and men with MDD. Men with MDD showed decreased ReHo value in the right median cingulate gyrus compared with HC males and increased ReHo value in the left dorsolateral superior frontal gyrus compared with HC males, we also found that HC males showed higher ReHo value in the right median cingulate gyrus than HC females.

CONCLUSIONS

Men and women do have sex differences in brain function, the occipital lobe, calcarine, DLPFC, and DCG were the main different brain regions found between male and female in MDD, which may be the biomarker brain regions that can help diagnose and treat MDD in men and women.

Alterations of regional spontaneous brain activities in anxiety disorders: A meta-analysis

BACKGROUND

Recent resting-state functional magnetic resonance imaging studies have provided strong evidence of abnormal regional spontaneous brain activities among anxiety-disordered patients. However, the evidence has been divergent and inconclusive. Therefore, it is necessary to perform a meta-analysis identifying a common pattern of altered regional spontaneous brain activity for anxiety disorders.

METHOD

Corresponding research of anxiety disorders, namely, whole-brain rs-fMRI studies that measured differences in regional homogeneity, amplitude of low-frequency fluctuations, or fractional amplitude of low-frequency fluctuations, were analyzed in this study. Overall, seven studies with 235 anxiety-disordered patients and 241 healthy controls were ultimately included in the meta-analysis. The meta-analysis was processed by seed-based d mapping.

RESULTS

Compared with healthy controls, patients with anxiety disorders showed significantly decreased regional spontaneous brain activities in the right putamen, the right orbital inferior frontal gyrus, and the right temporal pole. No increases in regional spontaneous brain activities were detected in patients relative to the controls.

LIMITATION

Limited number of available studies, only Asian samples, and insufficient information of sample characteristics.

CONCLUSION

The present study suggests that anxiety disorders are associated with aberrant regional brain activity in areas connected with emotion processing, which extends our understanding of anxiety disorders' pathophysiology.

Spontaneous cortical MEG activity undergoes unique age- and sex-related changes during the transition to adolescence

Background:

While numerous studies have examined the developmental trajectory of task-based neural oscillations during childhood and adolescence, far less is known about the evolution of spontaneous cortical activity during this time period. Likewise, many studies have shown robust sex differences in task-based oscillations during this developmental period, but whether such sex differences extend to spontaneous activity is not understood.

Methods:

Herein, we examined spontaneous cortical activity in 111 typically-developing youth (ages 9–15 years; 55 male). Participants completed a resting state magnetoencephalographic (MEG) recording and a structural MRI. MEG data were source imaged and the power within five canonical frequency bands (delta, theta, alpha, beta, gamma) was computed. The resulting power spectral density maps were analyzed via vertex-wise ANCOVAs to identify spatially-specific effects of age, sex, and their interaction.

Results:

We found robust increases in power with age in all frequencies except delta, which decreased over time, with findings largely confined to frontal cortices. Sex effects were distributed across frontal and temporal regions; females tended to have greater delta and beta power, whereas males had greater alpha. Importantly, there was a significant age-by-sex interaction in theta power, such that males exhibited decreasing power with age while females showed increasing power with age in the bilateral superior temporal cortices.

Discussion:

These data suggest that the strength of spontaneous activity undergoes robust change during the transition from childhood to adolescence (i.e., puberty onset), with intriguing sex differences in some cortical areas. Future developmental studies should probe task-related oscillations and spontaneous activity in parallel.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis

Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.

Advances in resting state fMRI acquisitions for functional connectomics

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

Ginkgo Biloba Extract Is Comparable With Donepezil in Improving Functional Recovery in Alzheimer's Disease: Results From a Multilevel Characterized Study Based on Clinical Features and Resting-State Functional Magnetic Resonance Imaging

Background: Ginkgo biloba extract (GBE) and donepezil have been reported to be effective in patients with Alzheimer’s disease (AD). Nonetheless, how these drugs impact spontaneous brain activities and how they consequently improve functional recovery are currently unclear.

Objectives: This study was to explore the efficacy of GBE vs. donepezil and their add-on efficacy on functional recovery and the adaption of spontaneous brain activities following pharmacologic treatment in patients with AD.

Methods: Patients with AD were enrolled and assigned to the GBE group (n = 50), the donepezil group (n = 50), or the combined group (n = 50). Neuropsychological assessments, including minimum mental state examination (MMSE), Alzheimer’s disease assessment scale-cognition (ADAS-Cog), instrumental activity of daily living (IADL), geriatric depression scale (GDS), neuropsychiatric inventory (NPI), and quality of life in Alzheimer’s disease (QOL-AD), were conducted at baseline, 1 month, 3 months, and 6 months. Resting-state functional magnetic resonance imaging (rs-fMRI) was collected to compare the amplitude of low-frequency fluctuation (ALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) at baseline and 6 months.

Results: No major significant differences were detected in all comparisons between groups across all follow-up time points. For intragroup comparison, MMSE and ADAS-Cog scores differed significantly across all follow-ups in three groups. The combined group showed significant improvement of GDS scores between baseline and 6 months (p = 0.007). The GBE group (p = 0.044) and donepezil group (p = 0.012) demonstrated significant improvement of NPI scores between baseline and 6 months. Significant correlations were observed between IADL and ALFF in the right gyrus rectus (p = 0.03) and in the left superior cerebellum gyrus (p = 0.01), between GDS and ALFF in the right middle temporal gyrus (p = 0.01), between NPI and PerAF in the left fusiform gyrus (p = 0.03), and between MMSE and ReHo in right superior frontal gyrus (p = 0.04).

Conclusion: GBE was comparable with donepezil in the improvement of functional recovery in patients with AD while the combined application of GBE and donepezil seems unnecessary. GBE-mediated improvement of functional recovery was characterized by decreased ALFF values in the right gyrus rectus and decreased PerAF values in the left fusiform gyrus. These featured variations of imaging metrics in specific brain regions may serve as biomarkers in the monitoring of the therapeutic efficacy of GBE.

Alterations of Regional Homogeneity in the Mild and Moderate Stages of Parkinson's Disease

Objectives: This study aimed to investigate alterations in regional homogeneity (ReHo) in early Parkinson's disease (PD) at different Hoehn and Yahr (HY) stages and to demonstrate the relationships between altered brain regions and clinical scale scores. Methods: We recruited 75 PD patients, including 43 with mild PD (PD-mild; HY stage: 1.0-1.5) and 32 with moderate PD (PD-moderate; HY stage: 2.0-2.5). We also recruited 37 age- and sex-matched healthy subjects as healthy controls (HC). All subjects underwent neuropsychological assessments and a 3.0 Tesla magnetic resonance scanning. Regional homogeneity of blood oxygen level-dependent (BOLD) signals was used to characterize regional cerebral function. Correlative relationships between mean ReHo values and clinical data were then explored. Results: Compared to the HC group, the PD-mild group exhibited increased ReHo values in the right cerebellum, while the PD-moderate group exhibited increased ReHo values in the bilateral cerebellum, and decreased ReHo values in the right superior temporal gyrus, the right Rolandic operculum, the right postcentral gyrus, and the right precentral gyrus. Reho value of right Pre/Postcentral was negatively correlated with HY stage. Compared to the PD-moderate group, the PD-mild group showed reduced ReHo values in the right superior orbital gyrus and the right rectus, in which the ReHo value was negatively correlated with cognition. Conclusion: The right superior orbital gyrus and right rectus may serve as a differential indicator for mild and moderate PD. Subjects with moderate PD had a greater scope for ReHo alterations in the cortex and compensation in the cerebellum than those with mild PD. PD at HY stages of 2.0-2.5 may already be classified as Braak stages 5 and 6 in terms of pathology. Our study revealed the different patterns of brain function in a resting state in PD at different HY stages and may help to elucidate the neural function and early diagnosis of patients with PD.

Resting-State Functional Connectivity in Mathematical Expertise

To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal-thalamic-temporal connections for mathematicians and the medial-frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections' characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts' successful performance.

Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

Functional neurological disorder (FND) was of great interest to early clinical neuroscience leaders. During the 20th century, neurology and psychiatry grew apart - leaving FND a borderland condition. Fortunately, a renaissance has occurred in the last two decades, fostered by increased recognition that FND is prevalent and diagnosed using "rule-in" examination signs. The parallel use of scientific tools to bridge brain structure - function relationships has helped refine an integrated biopsychosocial framework through which to conceptualize FND. In particular, a growing number of quality neuroimaging studies using a variety of methodologies have shed light on the emerging pathophysiology of FND. This renewed scientific interest has occurred in parallel with enhanced interdisciplinary collaborations, as illustrated by new care models combining psychological and physical therapies and the creation of a new multidisciplinary FND society supporting knowledge dissemination in the field. Within this context, this article summarizes the output of the first International FND Neuroimaging Workgroup meeting, held virtually, on June 17th, 2020 to appraise the state of neuroimaging research in the field and to catalyze large-scale collaborations. We first briefly summarize neural circuit models of FND, and then detail the research approaches used to date in FND within core content areas: cohort characterization; control group considerations; task-based functional neuroimaging; resting-state networks; structural neuroimaging; biomarkers of symptom severity and risk of illness; and predictors of treatment response and prognosis. Lastly, we outline a neuroimaging-focused research agenda to elucidate the pathophysiology of FND and aid the development of novel biologically and psychologically-informed treatments.

Brain Structure and Function of Chronic Low Back Pain Patients on Long-Term Opioid Analgesic Treatment: A Preliminary Study

Chronic low back pain (CLBP) is often treated with opioid analgesics (OA), a class of medications associated with a significant risk of misuse. However, little is known about how treatment with OA affect the brain in chronic pain patients. Gaining this knowledge is a necessary first step towards understanding OA associated analgesia and elucidating long-term risk of OA misuse. Here we study CLBP patients chronically medicated with opioids without any evidence of misuse and compare them to CLBP patients not on opioids and to healthy controls using structural and functional brain imaging. CLBP patients medicated with OA showed loss of volume in the nucleus accumbens and thalamus, and an overall significant decrease in signal to noise ratio in their sub-cortical areas. Power spectral density analysis (PSD) of frequency content in the accumbens’ resting state activity revealed that both medicated and unmedicated patients showed loss of PSD within the slow-5 frequency band (0.01–0.027 Hz) while only CLBP patients on OA showed additional density loss within the slow-4 frequency band (0.027–0.073 Hz). We conclude that chronic treatment with OA is associated with altered brain structure and function within sensory limbic areas.

Brain Activations and Functional Connectivity Patterns Associated with Insight-Based and Analytical Anagram Solving

Insight is one of the most mysterious problem-solving phenomena involving the sudden emergence of a solution, often preceded by long unproductive attempts to find it. This seemingly unexplainable generation of the answer, together with the role attributed to insight in the advancement of science, technology and culture, stimulate active research interest in discovering its neuronal underpinnings. The present study employs functional Magnetic resonance imaging (fMRI) to probe and compare the brain activations occurring in the course of solving anagrams by insight or analytically, as judged by the subjects. A number of regions were activated in both strategies, including the left premotor cortex, left claustrum, and bilateral clusters in the precuneus and middle temporal gyrus. The activated areas span the majority of the clusters reported in a recent meta-analysis of insight-related fMRI studies. At the same time, the activation patterns were very similar between the insight and analytical solutions, with the only difference in the right sensorimotor region probably explainable by subject motion related to the study design. Additionally, we applied resting-state fMRI to study functional connectivity patterns correlated with the individual frequency of insight anagram solutions. Significant correlations were found for the seed-based connectivity of areas in the left premotor cortex, left claustrum, and left frontal eye field. The results stress the need for optimizing insight paradigms with respect to the accuracy and reliability of the subjective insight/analytical solution classification. Furthermore, the short-lived nature of the insight phenomenon makes it difficult to capture the associated neural events with the current experimental techniques and motivates complementing such studies by the investigation of the structural and functional brain features related to the individual differences in the frequency of insight-based decisions.