Linking graph features of anatomical architecture to regional brain activity: A multi-modal MRI study

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Graph theory approach for the structural-functional brain connectome of depression

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

Dynamic alterations of amplitude of low-frequency fluctuations in patients with chronic neck pain

Background

The pathogenesis of neck pain in the brain, which is the fourth most common cause of disability, remains unclear. Furthermore, little is known about the characteristics of dynamic local functional brain activity in cervical pain.

Objective

The present study aimed to investigate the changes of local brain activity caused by chronic neck pain and the factors leading to neck pain.

Methods

Using the amplitude of low-frequency fluctuations (ALFF) method combined with sliding window approach, we compared local brain activity that was measured by the functional magnetic resonance imaging (fMRI) of 107 patients with chronic neck pain (CNP) with that of 57 healthy control participants. Five pathogenic factors were selected for correlation analysis.

Results

The group comparison results of dynamic amplitude of low-frequency fluctuation (dALFF) variability showed that patients with CNP exhibited decreased dALFF variability in the left inferior temporal gyrus, the middle temporal gyrus, the angular gyrus, the inferior parietal marginal angular gyrus, and the middle occipital gyrus. The abnormal dALFF variability of the left inferior temporal gyrus was negatively correlated with the average daily working hours of patients with neck pain.

Conclusions

The findings indicated that the brain regions of patients with CNP responsible for audition, vision, memory, and emotion were subjected to temporal variability of abnormal regional brain activity. Moreover, the dALFF variability in the left inferior temporal gyrus might be a risk factor for neck pain.This study revealed the brain dysfunction of patients with CNP from the perspective of dynamic local brain activity, and highlighted the important role of dALFF variability in understanding the neural mechanism of CNP.

Connectivity Patterns in the Core Resting-State Networks and Their Influence on Cognition

Introduction: Three prominent resting-state networks (rsNW) (default mode network [DMN], salience network [SN], and central executive network [CEN]) are recognized for their important role in several neuropsychiatric conditions. However, our understanding of their relevance in terms of cognition remains insufficient. Materials and Methods: In response, this study aims at investigating the patterns of different network properties (resting-state activity [RSA] and short- and long-range functional connectivity [FC]) in these three core rsNWs, as well as the dynamics of age-associated changes and their relation to cognitive performance in a sample of healthy controls (N = 74) covering a large age span (20-79 years). Using a whole-network based approach, three measures were calculated from the functional magnetic resonance imaging (fMRI) data: amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree of network centrality (DC). The cognitive test battery covered the following domains: memory, executive functioning, processing speed, attention, and visual perception. Results: For all three fMRI measures (ALFF, ReHo, and DC), the highest values of spontaneous brain activity (ALFF), short- and long-range connectivity (ReHo, DC) were observed in the DMN and the lowest in the SN. Significant age-associated decrease was observed in the DMN for ALFF and DC, and in the SN for ALFF and ReHo. Significant negative partial correlations were observed for working memory and ALFF in all three networks, as well as for additional cognitive parameters and ALFF in CEN. Discussion: Our results show that higher RSA in the three core rsNWs may have an unfavorable effect on cognition. Conversely, the pattern of network properties in healthy subjects included low RSA and FC in the SN. This complements previous research related to the three core rsNW and shows that the chosen approach can provide additional insight into their function.

Associations between Neighborhood SES and Functional Brain Network Development

Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.

Shared and specific dynamics of brain segregation and integration in bipolar disorder and major depressive disorder: A resting-state functional magnetic resonance imaging study

BACKGROUND

When bipolar disorder (BD) presents as the depressive state, it is often misdiagnosed as major depressive disorder (MDD). However, few studies have focused on dynamic differences in local brain activity and connectivity between BD and MDD. Therefore, the present study explored shared and specific patterns of abnormal dynamic brain segregation and integration in BD and MDD patients.

METHODS

BD Patients (n = 106), MDD patients (n = 114), and 130 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (fMRI). We first used a sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF) and, based on the altered dALFF, further analyzed the dynamic functional connectivity (dFC) using a seed-based approach.

RESULTS

Both the BD and MDD groups showed decreased temporal variability of the dALFF (less dynamic segregation) in the bilateral posterior cingulate cortex (PCC)/precuneus compared with the HCs. The MDD group showed increased temporal variability of the dALFF (more dynamic segregation) in the left putamen compared with the controls, but there was no significant difference between the BD and HCs. The dFC analysis also showed that both the BD and MDD groups had reduced dFC (less dynamic integration) between the bilateral PCC/ precuneus and the left inferior parietal lobule compared with the HCs.

LIMITATIONS

This study was cross-sectional and did not examine data from remitted BD and MDD patients.

CONCLUSION

Our findings indicated disrupted dynamic balance between segregation and integration within the default mode network in both BD and MDD. Moreover, we found MDD-specific abnormal brain dynamics in the putamen.

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

Cognitive Control Network Homogeneity and Executive Functions in Late-Life Depression

BACKGROUND

Late-life depression is characterized by network abnormalities, especially within the cognitive control network. We used alternative functional connectivity approaches, regional homogeneity (ReHo) and network homogeneity, to investigate late-life depression functional homogeneity. We examined the association between cognitive control network homogeneity and executive functions.

METHODS

Resting-state functional magnetic resonance imaging data were analyzed for 33 older adults with depression and 43 healthy control subjects. ReHo was performed as the correlation between each voxel and the 27 neighbor voxels. Network homogeneity was calculated as global brain connectivity restricted to 7 networks. T-maps were generated for group comparisons. We measured cognitive performance and executive functions with the Dementia Rating Scale, Trail-Making Test (A and B), Stroop Color Word Test, and Digit Span Test.

RESULTS

Older adults with depression showed increased ReHo in the bilateral dorsal anterior cingulate cortex (dACC) and the right middle temporal gyrus, with no significant findings for network homogeneity. Hierarchical linear regression models showed that higher ReHo in the dACC predicted better performance on Trail-Making Test B (p < .001; R2 = .49), Digit Span Backward (p < .05; R2 = .23), and Digit Span Total (p < .05; R2 = .23). Used as a seed, the dACC cluster of higher ReHo showed lower functional connectivity with bilateral precuneus.

CONCLUSIONS

Higher ReHo within the dACC and right middle temporal gyrus distinguish older adults with depression from control subjects. The correlations with executive function performance support increased ReHo in the dACC as a meaningful measure of the organization of the cognitive control network and a potential compensatory mechanism. Lower functional connectivity between the dACC and the precuneus in late-life depression suggests that clusters of increased ReHo may be functionally segregated.

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

Altered dynamics of brain segregation and integration in poststroke aphasia

Poststroke aphasia (PSA) results from direct effect of focal lesions and dysfunction of distributed language networks. However, how flexible the activity at specific nodes control global dynamics is currently unknown. In this study, we demonstrate that alterations in the regional activity may cause imbalances between segregation and integration in temporo-spatial pattern, and the transient dynamics are disrupted in PSA patients. Specifically, we applied dynamic framework to eyes-closed resting-state functional MRI data from PSA patients (n = 17), and age-, gender-, and education-matched healthy controls (HCs, n = 20). Subsequently, we calculated two basis brain organizational principles: "dynamic segregation," obtained from dynamic amplitude of low-frequency fluctuations (dALFF), which represent the specialized processing within interconnected brain regions; and "dynamic integration," obtained from dynamic functional connectivity, which measures the efficient communication between interconnected brain regions. We found that both measures were decreased in the PSA patients within the left frontal and temporal subregions compared to the HCs. PSA patients displayed increased flexibility of interaction between left temporo-frontal subregions and right temporo-parieto-frontal subnetworks. Furthermore, we found that dALFF in the pars triangularis of left inferior frontal gyrus was associated with aphasia quotient. These findings suggest that the reduced temporal flexibility of regional activity in language-relevant cortical regions in PSA is related to the disrupted organization of intrahemispheric networks, leading to a loss of the corresponding functions. By using dynamic framework, our results offer valuable information about the alterations in segregation and integration of spatiotemporal information across networks and illuminate how dysfunction in flexible activity may underlie language deficits in PSA.

Disrupted Brain Entropy And Functional Connectivity Patterns Of Thalamic Subregions In Major Depressive Disorder

PURPOSE

Entropy analysis of resting-state functional magnetic resonance imaging (R-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. Thalamus-related dysfunction might be a potential trait marker of major depressive disorder (MDD), but the abnormal changes in the thalamus based on R-fMRI are still unclear from the perspective of brain temporal dynamics. The aim of this study was to identify local entropy changes and subregional connectivity patterns of the thalamus in MDD patients.

PATIENTS AND METHODS

We measured the sample entropy of the R-fMRI data from 46 MDD patients and 32 matched healthy controls. We employed the Louvain method for the module detection algorithm to automatically identify a functional parcellation of the thalamus and then examined the whole-brain subregional connectivity patterns.

RESULTS

The results indicated that the MDD patients had decreased entropy in the bilateral thalami compared with healthy controls. Increased functional connectivity between the thalamic subregions and the medial part of the superior frontal gyrus (mSFG) was found in MDD patients.

CONCLUSION

This study showed new evidence about sample entropy changes in MDD patients. The functional connectivity alterations that were widely distributed across almost all the thalamic subregions with the mSFG in MDD suggest a general involvement independent of the location and function of the subregions.

Resting state functional connectivity of the amygdala and problem drinking in non-dependent alcohol drinkers

Alcohol misuse is associated with dysfunction of the amygdala-prefrontal cortical circuit. The amygdala and its cortical targets show decreased activity during a variety of task challenges in individuals engaged in problem drinking. On the other hand, it is less clear how amygdala resting state functional connectivity (rsFC) may be altered in association with alcohol misuse and whether such changes are restricted to prefrontal cortical structures. Further, the influences of comorbid substance use and depression and potential sex differences have not been assessed in earlier work. Here, with fMRI data from a Nathan Kline Institute/Rockland sample of 83 non-dependent alcohol drinkers (26 men), we addressed changes in whole brain rsFC of the amygdala in association with problem drinking as indexed by an alcohol involvement score. Imaging data were processed with Statistical Parametric Mapping following standard routines and all results were examined at voxel p < 0.001 uncorrected in combination with cluster p < 0.05 corrected for false discovery rate. Alcohol misuse was correlated with decreased amygdala connectivity with the dorsal anterior cingulate cortex (dACC) irrespective of depression and other substance use. Changes in amygdala-dACC connectivity manifested in the latero-basal subdivision of the amygdala. Further, men as compared to women showed a significantly stronger relationship in decreased amygdala-dACC connectivity and problem drinking, although it should be noted that men also showed a trend toward higher alcohol involvement score than women. The findings add to a growing literature documenting disrupted amygdala-prefrontal cortical functions in relation to alcohol misuse.