Cluster-based statistics for brain connectivity in correlation with behavioral measures

Node-layer duality in networked systems

Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine connectivity from either a node-centric or layer-centric viewpoint. Through rigorous analytical methods and extensive simulations, we demonstrate that nodewise and layerwise connectivity measure different but related aspects of the same system. Leveraging node-layer duality provides complementary insights, enabling a deeper comprehension of diverse networks across social science, technology and biology. Taken together, these findings reveal previously unappreciated features of complex systems and provide a fresh tool for delving into their structure and dynamics.

Abnormality of anxious behaviors and functional connectivity between the amygdala and the frontal lobe in maternally deprived monkeys

OBJECTIVE

Anxious behaviors often occur in individuals who have experienced early adversity. Anxious behaviors can bring many hazards, such as social withdrawal, eating disorders, negative self-efficacy, self-injurious thoughts and behaviors, anxiety disorders, and even depression. Abnormal behavior are is closely related to changes in corresponding circuit functions in the brain. This study investigated the relationship between brain circuits and anxious behaviors in maternal-deprived rhesus monkey animal model, which mimic early adversity in human.

METHODS

Twenty-five rhesus monkeys (Macaca mulatta) were grouped by two different rearing conditions: 11 normal control and mother-reared (MR) monkeys and 14 maternally deprived and peer-reared (MD) monkeys. After obtaining images of the brain areas with significant differences in maternal separation and normal control macaque function, the relationship between functional junction intensity and stereotypical behaviors was determined by correlation analysis.

RESULTS

The correlation analysis revealed that stereotypical behaviors were negatively correlated with the coupling between the left lateral amygdala subregion and the left inferior frontal gyrus in both MD and MR macaques.

CONCLUSION

This study suggests that early adversity-induced anxious behaviors are associated with changes in the strength of the amygdala-prefrontal connection. The normalization of the regions involved in the functional connection might reverse the behavioral abnormality. It provides a solid foundation for effective intervention in human early adversity.

SIGNIFICANCE STATEMENT

This study suggests that early adversity-induced anxious behaviors are associated with changes in the strength of the amygdala-prefrontal connection. The higher the amygdala-prefrontal connection strength, the less stereotyped behaviors exhibited by monkeys experiencing early adversity. Thus, in the future, changing the strength of the amygdala-prefrontal connection may reverse the behavioral abnormalities of individuals who experience early adversity. This study provides a solid foundation for effective intervention in humans' early adversity.

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Background

Brain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.

Objective

This study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.

Methods

This study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.

Results

Our proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.

Conclusion

Our unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

Developmental organization of neural dynamics supporting auditory perception

Purpose:

A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy. We also provided evidence demonstrating the maturation of an anterior-to-posterior functional division within the superior-temporal gyrus (STG), which is known to exist in the adult STG.

Methods:

We studied 32 patients undergoing extraoperative electrocorticography (age range: eight months to 28 years) and analyzed 2,039 intracranial electrode sites outside the seizure onset zone, interictal spike-generating areas, and MRI lesions. Patients were given forward (normal) speech sounds, backward-played speech sounds, and signal-correlated noises during a task-free condition. We then quantified sound processing-related neural costs at given time windows using high-gamma amplitude at 70–110 Hz and animated the group-level high-gamma dynamics on a spatially normalized three-dimensional brain surface. Finally, we determined if age independently contributed to high-gamma dynamics across brain regions and time windows.

Results:

Group-level analysis of noise-related neural costs in the STG revealed developmental enhancement of early high-gamma augmentation and diminution of delayed augmentation. Analysis of speech-related high-gamma activity demonstrated an anterior-to-posterior functional parcellation in the STG. The left anterior STG showed sustained augmentation throughout stimulus presentation, whereas the left posterior STG showed transient augmentation after stimulus onset. We found a double dissociation between the locations and developmental changes in speech sound-related high-gamma dynamics. Early left anterior STG high-gamma augmentation (i.e., within 200 ms post-stimulus onset) showed developmental enhancement, whereas delayed left posterior STG high-gamma augmentation declined with development.

Conclusions:

Our observations support the model that, with age, the human STG refines neural dynamics to rapidly detect and subsequently disregard uninformative acoustic noises. Our study also supports the notion that the anterior-to-posterior functional division within the left STG is gradually strengthened for efficient speech sound perception after birth.

Association of subcortical structural shapes with fatigue in neuromyelitis optica spectrum disorder

Although fatigue is a major symptom in patients with neuromyelitis optica spectrum disorder (NMOSD), the underlying mechanism remains unclear. We explored the relationship between subcortical structures and fatigue severity to identify neural substrates of fatigue in NMOSD. Clinical characteristics with brain magnetic resonance imaging were evaluated in forty patients with NMOSD. Fatigue was assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-fatigue) questionnaire (a higher score indicates less fatigue). We assessed the correlation between subcortical structures and fatigue severity using surface-based shape analysis. Most of the enrolled patients showed fatigue (72.5%; mean FACIT-fatigue score, 34.8 ± 10.8). The FACIT-fatigue score was negatively correlated with Expanded Disability Status Scale and Beck Depression Inventory scores (r = - 0.382, p = 0.016; r = - 0.578, p < 0.001). We observed that the right thalamus was the only extracted region for various threshold experiments. Further, patients with lower FACIT-fatigue scores (more fatigue) had decreased local shape volume in the right thalamus. Fatigue is common in patients with NMOSD, and atrophy in the right thalamus is strongly correlated with fatigue severity. The local shape volume of the right thalamus might serve as a biomarker of fatigue in NMOSD.

Effect of education on functional network edge efficiency in Alzheimer's disease

We investigated the effect of education on the edge efficiency in resting state functional networks (RSFNs) in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD). We collected the data of 57 early aMCI, 141 late aMCI, 173 mild ADD, and 39 moderate-to-severe ADD patients. We used years of education as a proxy for cognitive reserve. We measured edge efficiency for each edge in RSFNs, and performed simple slope analyses to discover their associations with education level among the four groups. In the late aMCI, a sub-network that had hub nodes in the right middle frontal gyrus and the right posterior cingulate gyrus, showed a positive association between RSFN edge efficiency and education (threshold = 2.5, p = 0.0478). There was no negative effect of education on the RSFN edge efficiency. In the early aMCI, mild ADD, and moderate-to-severe ADD, there were no sub-networks showing positive or negative correlation between education and RSFN edge efficiency. There was a positive effect of higher education on RSFN edge efficiency in the late aMCI, but not in the early aMCI or ADD. This indicates that in late aMCI, those who have higher education level have greater ability to resist collapsed functional network.

White matter tract-specific alterations in patients with primary restless legs syndrome

Prior diffusion tensor imaging (DTI) studies have investigated white matter (WM) changes in patients with primary restless legs syndrome (RLS), but the results were inconsistent. Here, we proposed using tract-specific statistical analysis (TSSA) to find alterations in specific WM tracts to clarify the pathophysiological mechanisms of RLS. We enrolled 30 patients with RLS and 31 age- and sex- matched controls who underwent brain magnetic resonance imaging, neuropsychological tests, and polysomnography. Fractional anisotropy (FA) maps obtained from whole-brain diffusion tensor imaging and TSSA were used to localize WM changes in patients with RLS. Subsequently, a comparison of FA values for each tract between patients and controls was performed. The associations between FA values and clinical, polysomnographic, and neuropsychological parameters in RLS patients were assessed. RLS patients demonstrated decreased FA values in the left corticospinal tract (CST) and cingulum, and in the right anterior thalamic radiation (ATR) and inferior fronto-occipital fasciculus (IFO). Patients’ attention/executive function and visual memory scores positively correlated with FA values in the right ATR, and anxiety levels negatively correlated with FA values in the right IFO. Additionally, the number of periodic leg movements and movement arousal index were negatively correlated with FA values in the left CST. The TSSA method identified previously unknown tract-specific alterations in patients with RLS and significant associations with distinct clinical manifestations of RLS.

White matter tract-specific alterations in male patients with untreated obstructive sleep apnea are associated with worse cognitive function

STUDY OBJECTIVES

Neurocognitive impairment is one of the daytime symptoms of obstructive sleep apnea (OSA). We proposed to use tract-specific statistical analysis (TSSA) to investigate whether there are fiber tract abnormalities in OSA, which may be undiscovered using voxel-based approaches, and whether such tract-specific disruptions in brain connectivity are associated with neuropsychological deficits in patients with untreated OSA.

METHODS

We enrolled 38 patients with OSA diagnosed by overnight polysomnography, and 41 healthy sleepers. Fractional anisotropy (FA) and mean diffusivity (MD) maps were obtained from whole-brain diffusion tensor imaging, and TSSA were used to assess regional deficits of white matter tracts. All participants underwent a battery of neuropsychological tests. To evaluate the association between FA values and clinical, polysomnographic, and neuropsychological parameters in the OSA group, permutation-based tests for correlation were performed preceding cluster-based statistics.

RESULTS

Compared to healthy controls, patients with OSA showed decreased values of FA in the left and right anterior thalamic radiations, and right uncinate fasciculus (UNC) (p < 0.001, p = 0.005, and p = 0.008, respectively). A lower score of digit span backward was associated with lower FA values of right UNC in the OSA group (p = 0.023). The Rey Complex Figure Test copy score revealed a positive correlation with FA values in the right UNC (p = 0.010).

CONCLUSIONS

The TSSA method indeed identified previously unrevealed tract-specific disruptions in OSA. Furthermore, reduced FA values in the frontal lobe portion of the right UNC which has been known to be involved in working memory function were significantly associated with lower cognitive performance in patients with untreated OSA.

Effects of Alzheimer's and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer's Disease

Early- and late-onset Alzheimer's disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [¹⁸F]-THK5351 PET, [¹⁸F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Microstructural Connectivity is More Related to Cognition than Conventional MRI in Parkinson's Disease

BACKGROUND

The different effects of white matter hyperintensity (WMH) severity and WMH-associated microstructural connectivity on cognition in the early stages of Parkinson's disease (PD) have not been investigated.

OBJECTIVE

To investigate the differential effect of WMH severity and WMH-associated microstructural connectivity on cognition in early stages of PD.

METHODS

A total of 136 de novo PD patients were enrolled and divided into groups based on total WMH visual rating scores as follows: mild, moderate, and severe. Microstructural connectivity was measured using graph theoretical analysis according to WMH severity. Additionally, correlation coefficients between WMH-associated microstructural connectivity or WMH scores and cognitive performance were assessed.

RESULTS

Patients with severe WMHs demonstrated poorer performance in language function than those with moderate WMHs, and in frontal/executive and visual memory function than those with mild WMHs. Areas of microstructural connectivity were more extensive in patients with severe WMHs compared to those with mild and moderate WMHs, involving frontal and parieto-temporal regions. WMH-associated right fronto-temporo-parietal microstructural disintegration was correlated with cognitive dysfunction in attention, frontal/executive, and memory domains, whereas there was no correlation between WMH scores and any cognitive domains.

CONCLUSION

These data suggest that disruption of microstructural networks by WMHs, rather than WMH burden itself, contributed more to cognitive impairment in PD.

Audiovisual interactions in speeded discrimination of a visual event

The integration of information from different senses is central to our perception of the external world. Audiovisual interactions have been particularly well studied in this context and various illusions have been developed to demonstrate strong influences of these interactions on the final percept. Using audiovisual paradigms, previous studies have shown that even task-irrelevant information provided by a secondary modality can change the detection and discrimination of a primary target. These modulations have been found to be significantly dependent on the relative timing between auditory and visual stimuli. Although these interactions in time have been commonly reported, we have still limited understanding of the relationship between the modulations of event-related potentials (ERPs) and final behavioral performance. Here, we aimed to shed light on this important issue by using a speeded discrimination paradigm combined with electroencephalogram (EEG). During the experimental sessions, the timing between an auditory click and a visual flash was varied over a wide range of stimulus onset asynchronies and observers were engaged in speeded discrimination of flash location. Behavioral reaction times were significantly changed by click timing. Furthermore, the modulations of evoked activities over medial parietal/parieto-occipital electrodes were associated with this effect. These modulations were within the 126-176 ms time range and more importantly, they were also correlated with the changes in reaction times. These results provide an important functional link between audiovisual interactions at early stages of sensory processing and reaction times. Together with previous research, they further suggest that early crossmodal interactions play a critical role in perceptual performance.

Association of Subcortical Structural Shapes With Tau, Amyloid, and Cortical Atrophy in Early-Onset and Late-Onset Alzheimer's Disease

The objectives of this study were to compare the topographical subcortical shape and to investigate the effects of tau or amyloid burden on atrophic patterns in early onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). One hundred and sixty-one participants (53 EOAD, 44 LOAD, 33 young controls, and 31 older controls) underwent [18F]THK5351 positron emission tomography (PET), [18F]flutemetamol (FLUTE) PET, and 3T MRI scans. We used surface-based analysis to evaluate subcortical structural shape, permutation-based statistics for group comparisons, and Spearman's correlations to determine associations with THK, FLUTE, cortical thickness, and neuropsychological test results. When compared to their age-matched controls, EOAD patients exhibited shape reduction in the bilateral amygdala, hippocampus, caudate, and putamen, while in LOAD patients, the bilateral amygdala and hippocampus showed decreased shapes. In EOAD, widespread subcortical shrinkage, with less association of the hippocampus, correlated with THK retention and cortical thinning, while in LOAD patients, subcortical structures were limited which had significant correlation with THK or mean cortical thickness. Subcortical structural shape showed less correlation with FLUTE global retention in both EOAD and LOAD. Multiple cognitive domains, except memory function, correlated with the bilateral amygdala, caudate, and putamen in EOAD patients, while more restricted regions in the subcortical structures were correlated with neuropsychological test results in LOAD patients. Subcortical structures were associated with AD hallmarks in EOAD. However, the correlation was limited in LOAD. Moreover, relationship between subcortical structural atrophy and cognitive decline were quite different between EOAD and LOAD. These findings suggest that the effects of Alzheimer's pathologies on subcortical structural changes in EOAD and LOAD and they may have different courses of pathomechanism.

Local shape volume alterations in subcortical structures of suicide attempters with major depressive disorder

Suicide is among the most important global health concerns; accordingly, an increasing number of studies have shown the risks for suicide attempt(s) in terms of brain morphometric features and their clinical correlates. However, brain studies addressing suicidal vulnerability have been more focused on demonstrating impairments in cortical structures than in the subcortical structures. Using local shape volumes (LSV) analysis, we investigated subcortical structures with their clinical correlates in depressed patients who attempted suicide. Then we compared them with depressed patients without a suicidal history and age- and sex-matched healthy controls (HCs; i.e., 47 suicide attempters with depression, 47 non-suicide attempters with depression, and 109 HCs). Significant volumetric differences were found between suicidal and nonsuicidal depressed patients in several vertices: 16 in the left amygdala; 201 in the left hippocampus; 1,057 in the left putamen; and 140 in the left pallidum; 1 in the right pallidum; and 6 in the bilateral thalamus. These findings indicated subcortical alterations in LSV in components of the limbic-cortical-striatal-pallidal-thalamic circuits. Moreover, our results demonstrated that the basal ganglia was correlated with perceived stress levels, and the thalamus was correlated with suicidal ideation. We suggest that suicidality in major depressive disorder may involve subcortical volume alterations.

Neurobiological influence of comorbid conditions in young patients diagnosed with gaming disorder: A whole-brain functional connectivity study based on a data driven method

Gaming disorder, which is characterized by multiple cognitive and behavioral symptoms, often has comorbid psychiatric conditions such as depression and attention-deficit hyperactivity disorder. Neurobiological effects of the comorbid disorders so far reported are not converging, exhibiting positive and negative alterations of the connectivity in brain networks. In this study, we conducted resting-state functional magnetic-resonance imaging and whole brain functional connectivity analyses for young participants consisting of 40 patients diagnosed with the gaming disorder, with and without comorbid conditions, and 29 healthy controls. Compared to healthy controls, the gaming disorder-alone patients had partially diminished connectivities in the reward system and executive control network, within which there existed central nodes that served as a hub of diminished connections. In the gaming disorder patients who had comorbidity of autism spectrum disorder, the diminished connections were enlarged, with alteration of the hub nodes, to the entire brain areas involved in the reward system including cortical, subcortical and limbic areas that are crucial for reward processing, and to the whole cortical areas composing the executive control network. These observations suggest that the neurodevelopmental condition coexisting with the gaming disorder induced substantial impairment of the neural organizations associated with executive/cognitive and emotional functions, which are plausibly causal to the behavioral addiction, by rearranging and diminishing functional connectivities in the network.

Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment

Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer's disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed inter-network high-order FC (IN-HOFC), as a useful complement to the traditional inter-network FC methods, for characterizing more complex organizations among the large-scale brain networks. In the IN-HOFC, both network definition and inter-network FC are defined in a high-order manner. To test whether IN-HOFC is more sensitive to cognition decline due to brain diseases than traditional inter-network FC, 77 mild cognitive impairments (MCIs) and 89 controls are compared among the conventional methods and our IN-HOFC. The result shows that IN-HOFCs among three temporal lobe-related high-order networks are dampened in MCIs. The impairment of IN-HOFC is especially found between the anterior and posterior medial temporal lobe and could be a potential MCI biomarker at the network level. The competing network-level low-order FC methods, however, either revealing less or failing to detect any group difference. This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies.

Neural network correlates of high-altitude adaptive genetic variants in Tibetans: A pilot, exploratory study

Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI

BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.

Deformities of the Globus Pallidus are Associated with Severity of Suicidal Ideation and Impulsivity in Patients with Major Depressive Disorder

Neuroimaging research increasingly suggests there are biological features related to suicidal risk, including brain morphometric features, leading to an elaborate suicide risk assessment. However, few studies have focused on the severity of suicidal ideation and its association with subcortical anatomy in patients with major depressive disorder (MDD). Here, we mainly investigated whether specific structural differences were present in MDD patients with and without suicidal ideation; and supplemented comparison with and without suicidal attempt. We hypothesized that structures associated with suicidal ideation would be derived from a combination of depression and impulsivity. Local atrophy of subcortical structures in 48 patients with MDD (24 with suicidal ideation and 24 without) and 25 age- and sex-matched healthy controls were compared using a surface-based shape analysis method. There was no difference in brain volume between MDD patients with or without suicidal ideations; or MDD patients with or without suicidal attempt. However, the atrophy level in the left pallidum showed a positive correlation with severity of suicidal risk in MDD patients with suicidal ideation. Local atrophy of the left hippocampus, right caudate, and right pallidum had a positive correlation with total impulsivity. These findings possibly suggest that vulnerability to suicidal attempt can be derived from suicidal ideation combined with depression and impulsivity, related to reduced motivational control.

Dysautonomia is associated with structural and functional alterations in Parkinson disease

Objective

To investigate whether the presence of autonomic dysfunction is associated with white matter and functional connectivities and the level of cognitive performance in patients with de novo Parkinson disease (PD).

Methods

Seventy-five patients with de novo PD underwent a comprehensive autonomic function test and were classified into 2 groups according to the Composite Autonomic Severity Score (CASS; 30 with moderate to severe autonomic dysfunction [CASS 4–10, PD-AUT+] and 45 without significant autonomic dysfunction [CASS 0–3, PD-AUT−]). Network-based statistics and a graph theoretical analysis were performed to assess the interregional white matter connectivity using diffusion tensor imaging. We also performed analyses of resting-state functional connectivity and compared cognitive performance between the 2 groups.

Results

There were no significant differences in demographic characteristics and vascular risk factors between the PD-AUT+ and PD-AUT− groups. The PD-AUT+ group showed poorer cognitive performance on frontal/executive function than the PD-AUT− group. The PD-AUT+ group exhibited severely disrupted white matter connectivity in both fronto-subcortical and posterior cortical regions, which was well correlated with the severity of autonomic dysfunction assessed by the CASS. In addition, functional connectivity within the executive control network and dorsal attention network negatively correlated with the CASS.

Conclusions

Our results suggest that autonomic dysfunction is associated with disrupted white matter and functional brain connectivity as well as cognitive impairment in de novo patients with PD.

Multiple cortical thickness sub-networks and cognitive impairments in first episode, drug naïve patients with late life depression: A graph theory analysis

BACKGROUND

Coordinated and pattern-wise changes in large scale gray matter structural networks reflect neural circuitry dysfunction in late life depression (LLD), which in turn is associated with emotional dysregulation and cognitive impairments. However, due to methodological limitations, there have been few attempts made to identify individual-level structural network properties or sub-networks that are involved in important brain functions in LLD.

METHODS

In this study, we sought to construct individual-level gray matter structural networks using average cortical thicknesses of several brain areas to investigate the characteristics of the gray matter structural networks in normal controls and LLD patients. Additionally, we investigated the structural sub-networks correlated with several clinical measurements including cognitive impairment and depression severity.

RESULTS

We observed that small worldness, clustering coefficients, global and local efficiency, and hub structures in the brains of LLD patients were significantly different from healthy controls. We further found that a sub-network including the anterior cingulate, dorsolateral prefrontal cortex and superior prefrontal cortex is significantly associated with attention control and executive function. The severity of depression was associated with the sub-networks comprising the salience network, including the anterior cingulate and insula.

LIMITATIONS

We investigated cortico-cortical connectivity, but omitted the subcortical structures such as the striatum and thalamus.

CONCLUSION

We report differences in patterns between several clinical measurements and sub-networks from large-scale and individual-level cortical thickness networks in LLD.

Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.

Subcortical shape analysis of progressive mild cognitive impairment in Parkinson's disease

BACKGROUND

Cortical neural correlates of ongoing cognitive decline in Parkinson's disease (PD) have been suggested; however, the role of subcortical structures in longitudinal change of cognitive dysfunction in PD has not been fully investigated. Here, we used automatic analysis to explore subcortical brain structures in patients with PD with mild cognitive impairment that converts into PD with dementia.

METHODS

One hundred eighty-two patients with PD with mild cognitive impairment were classified as PD with mild cognitive impairment converters (n = 74) or nonconverters (n = 108), depending on whether they were subsequently diagnosed with dementia in PD. We used surface-based analysis to compare atrophic changes of subcortical brain structures between PD with mild cognitive impairment converters and nonconverters.

RESULTS

PD with mild cognitive impairment converters had lower cognitive composite scores in the attention and frontal executive domains than did nonconverters. Subcortical shape analysis revealed that PD with mild cognitive impairment converters had smaller local shape volumes than did nonconverters in the bilateral thalamus, right caudate, and right hippocampus. Logistic regression analysis showed that local shape volumes in the bilateral thalamus and right caudate were significant independent predictors of PD with mild cognitive impairment converters. In the PD with mild cognitive impairment converter group, thalamic local shape volume was associated with semantic fluency and attentional composite score.

CONCLUSIONS

The present data suggest that the local shape volumes of deep subcortical structures, especially in the caudate and thalamus, may serve as important predictors of the development of dementia in patients with PD. © 2017 International Parkinson and Movement Disorder Society.

Neural substrates of motor and cognitive dysfunctions in SCA2 patients: A network based statistics analysis

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease characterized by a progressive cerebellar syndrome, which can be isolated or associated with extracerebellar signs. It has been shown that patients affected by SCA2 present also cognitive impairments and psychiatric symptoms.

The cerebellum is known to modulate cortical activity and to contribute to distinct functional networks related to higher-level functions beyond motor control. It is therefore conceivable that one or more networks, rather than isolated regions, may be dysfunctional in cerebellar degenerative diseases and that an abnormal connectivity within specific cerebello-cortical regions might explain the widespread deficits typically observed in patients.

In the present study, the network-based statistics (NBS) approach was used to assess differences in functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes.

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A cerebellar dysfunction affects long-distance cerebral regions in SCA2 patients.

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Connectivity changes affect sensorimotor and cognitive cerebello-cortical nodes.

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Cerebellar symptoms may be related to altered cerebello-cerebral connectivity.

Degree-based statistic and center persistency for brain connectivity analysis

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc.

Tract-Specific Correlates of Neuropsychological Deficits in Patients with Subcortical Vascular Cognitive Impairment

The white matter tract-specific correlates of neuropsychological deficits are not fully established in patients with subcortical vascular cognitive impairment (SVCI), where white matter tract damage may be a critical factor in cognitive impairment. The purpose of this study is to investigate the tract-specific correlates of neuropsychological deficits in SVCI patients using tract-specific statistical analysis (TSSA). We prospectively recruited 114 SVCI patients, and 55 age-, gender-, and education-matched individuals with normal cognition (NC). All participants underwent diffusion weighted imaging and neuropsychological testing. We classified tractography results into fourteen major fiber tracts and analyzed group comparison and correlation with cognitive impairments. Relative to NC subjects, SVCI patients showed decreased fractional anisotropy values in bilateral anterior-thalamic radiation, cingulum, superior-longitudinal fasciculus, uncinate fasciculus, corticospinal tract, and left inferior-longitudinal fasciculus. Focal disruptions in specific tracts were associated with specific cognitive impairments. Our findings suggest that disconnection of specific white matter tracts, especially those neighboring and providing connections between gray matter regions important to certain cognitive functions, may contribute to specific cognitive impairments in SVCI.

Decoupling of large-scale brain networks supports the consolidation of durable episodic memories

At a large scale, the human brain is organized into modules of interconnected regions, some of which play opposing roles in supporting cognition. In particular, the Default-Mode Network (DMN) has been linked to operations on internal representations, while task-positive networks are recruited during interactions with the external world. Here, we test the hypothesis that the generation of durable long-term memories depends on optimal recruitment of such antagonistic large-scale networks. As long-term memory consolidation is a process ongoing for days and weeks after an experience, we propose that individuals characterized by strong decoupling of the DMN and task-positive networks at rest operate in a mode beneficial for the long-term stabilization of episodic memories. To capture network connectivity unaffected by transient task demands and representative of brain behavior outside an experimental setting, 87 participants were scanned during rest before performing an associative encoding task. To link individual resting-state functional connectivity patterns to time-dependent memory consolidation processes, participants were given an unannounced memory test, either after a brief interval or after a retention period of ~6 weeks. We found that participants with a resting state characterized by high synchronicity in a DMN-centered network system and low synchronicity between task-positive networks showed superior recollection weeks after encoding. These relationships were not observed for information probed only hours after encoding. Furthermore, the two network systems were found to be anticorrelated. Our results suggest that this memory-relevant antagonism between DMN and task-positive networks is maintained through complex regulatory interactions between the systems.

Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency

Preoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills). Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

Reorganization of functionally connected brain subnetworks in high-functioning autism

Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Twenty-six subjects (13 with ASD) watched a 68-min movie during functional magnetic resonance imaging. For each subject, we computed Pearson's correlation between haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the whole-brain functional networks, we derived individual and group-level subnetworks using graph theory. Scaled inclusivity was then calculated between all subject pairs to estimate intersubject similarity of connectivity structure of each subnetwork. Additional 54 individuals (27 with ASD) from the ABIDE resting-state database were included to test the reproducibility of the results. Between-group differences were observed in the composition of default-mode and ventro-temporal-limbic (VTL) subnetworks. The VTL subnetwork included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior temporal gyri. Further, VTL subnetwork similarity between subject pairs correlated significantly with similarity of symptom gravity measured with autism quotient. This correlation was observed also within the controls, and in the reproducibility dataset with ADI-R and ADOS scores. Our results highlight how the reorganization of functional subnetworks in individuals with ASD clarifies the mixture of hypo- and hyper-connectivity findings. Importantly, only the functional organization of the VTL subnetwork emerges as a marker of inter-individual similarities that co-vary with behavioral measures across all participants. These findings suggest a pivotal role of ventro-temporal and limbic systems in autism.

On characterizing population commonalities and subject variations in brain networks

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.

Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment

AIMS

Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (svMCI). However, whether and how these changes alter functional brain network organization remains largely unknown.

METHODS

We recruited 21 patients with svMCI and 26 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks.

RESULTS

Compared with the HC individuals, the patients with svMCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the svMCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The svMCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the svMCI patients.

CONCLUSION

Together, our results indicate that svMCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of svMCI.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain's traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations.