Investigating amplitude of low-frequency fluctuation and possible links with cognitive impairment in childhood and adolescence onset schizophrenia: a correlation study

Background

Cognitive impairment (CI) is a distinctive characteristic of schizophrenia, with evidence suggesting that childhood and adolescence onset schizophrenia (CAOS), representing severe but rare forms of schizophrenia, share continuity with adult-onset conditions. While relationships between altered brain function and CI have been identified in adults with schizophrenia, the extent of brain function abnormalities in CAOS remains largely unknown. In this study, we employed resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional alterations in brain areas among patients with CAOS. To assess CI across multiple cognitive domains, we utilized the Stroop Color and Word Tests (SCWT) and MATRICS Consensus Cognitive Battery (MCCB) tests. Our objective was to explore the associations between functional CI and the amplitude of low-frequency fluctuation (ALFF) levels in these patients.

Methods

We enrolled 50 patients diagnosed with CAOS and 33 healthy controls (HCs) matched for sex and age. Cognitive functions were assessed using the MCCB and SCWT methods. Rs-fMRI data were acquired using gradient-echo echo-planar imaging sequences. Voxel-based ALFF group maps were compared through two-sample t-tests in SPM8. Subsequently, correlation analyses were conducted to identify associations between ALFF levels and cognitive scores.

Results

In comparison to HCs, patients exhibited significantly increased ALFF levels in the right fusiform gyrus, frontal lobe, and caudate, as well as the left frontal lobe and caudate. Conversely, reduced ALFF levels were observed in the temporal and left medial frontal lobes. Significant differences were identified between HCs and patients in terms of total cognitive scores, ALFF levels, and domain scores. All test scores were decreased, except for TMA. Correlation analyses between ALFF levels and cognitive functions in patients with CAOS differed from those in HCs. Pearson correlation analyses revealed positive associations between Brief Visuospatial Memory Test - Revised (BVMT-R) scores and ALFF levels in the left medial frontal gyrus. Digital Span Test (DST) scores were negatively correlated with ALFF levels in the right caudate, and Maze Test values were negatively correlated with levels in the left caudate. However, Pearson correlation analyses in HCs indicated that color and Hopkins Verbal Learning Test (HVLT-R) scores positively correlated with ALFF levels in the left frontal lobe, while color-word and symbol coding scores negatively correlated with levels in the right caudate.

Conclusions

Altered ALFF levels in the brain may be linked to cognitive impairment (CI) in patients with CAOS. We highlighted the pathophysiology of schizophrenia and provide imaging evidence that could potentially aid in the diagnosis of CAOS.

Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed

Background

Fifty-one percent of individuals with multiple sclerosis (MS) develop cognitive impairment (CI) in information processing speed (IPS). Although IPS scores are associated with health and well-being, neural changes that underlie IPS impairments in MS are not understood. Resting state fMRI can provide insight into brain function changes underlying impairment in persons with MS.

Objectives

We aimed to assess functional connectivity (FC) differences in (i) persons with MS compared to healthy controls (HC), (ii) persons with both MS and CI (MS-CI) compared to HC, (iii) persons with MS that are cognitively preserved (MS-CP) compared to HC, (iv) MS-CI compared to MS-CP, and (v) in relation to cognition within the MS group.

Methods

We included 107 participants with MS (age 49.5 ± 12.9, 82% women), and 94 controls (age 37.9 ± 15.4, 66% women). Each participant was administered the Symbol Digit Modalities Test (SDMT) and underwent a resting state fMRI scan. The MS-CI group was created by applying a z-score cut-off of ≤ -1.5 to locally normalized SDMT scores. The MS-CP group was created by applying a z-score of ≥0. Control groups (HCMS-CI and HCMS-CP) were based on the nearest age-matched HC participants. A whole-brain ROI-to-ROI analysis was performed followed by specific contrasts and a regression analysis.

Results

Individuals with MS showed FC differences compared to HC that involved the cerebellum, visual and language-associated brain regions, and the thalamus, hippocampus, and basal ganglia. The MS-CI showed FC differences compared to HCMS-CI that involved the cerebellum, visual and language-associated areas, thalamus, and caudate. SDMT scores were correlated with FC between the cerebellum and lateral occipital cortex in MS. No differences were observed between the MS-CP and HCMS-CP or MS-CI and MS-CP groups.

Conclusion

Our findings emphasize FC changes of cerebellar, visual, and language-associated areas in persons with MS. These differences were apparent for (i) all MS participants compared to HC, (ii) MS-CI subgroup and their matched controls, and (iii) the association between FC and SDMT scores within the MS group. Our findings strongly suggest that future work that examines the associations between FC and IPS impairments in MS should focus on the involvement of these regions.

Connectivity reveals homology between the visual systems of the human and macaque brains

The visual systems of humans and nonhuman primates share many similarities in both anatomical and functional organization. Understanding the homology and differences between the two systems can provide important insights into the neural basis of visual perception and cognition. This research aims to investigate the homology between human and macaque visual systems based on connectivity, using diffusion tensor imaging and resting-state functional magnetic resonance imaging to construct structural and functional connectivity fingerprints of the visual systems in humans and macaques, and quantitatively analyze the connectivity patterns. By integrating multimodal magnetic resonance imaging, this research explored the homology and differences between the two systems. The results showed that 9 brain regions in the macaque visual system formed highly homologous mapping relationships with 11 brain regions in the human visual system, and the related brain regions between the two species showed highly structure homologous, with their functional organization being essentially conserved across species. Finally, this research generated a homology information map of the visual system for humans and macaques, providing a new perspective for subsequent cross-species analysis.

Aberrant modular segregation of brain networks in female patients with bulimia nervosa

OBJECTIVE

Bulimia nervosa (BN) is an eating disorder associated with the dysfunction of intrinsic brain networks. However, whether the network disruptions in BN patients manifest as dysconnectivity or imbalances of network modular segregation remains unclear.

METHOD

We collected data from 41 women with BN and 41 matched healthy control (HC) women. We performed graph theory analysis based on resting-state functional magnetic resonance imaging (RS-fMRI) data; then, we computed the participation coefficient (PC) among brain modules to characterize the modular segregation for the BN and HC groups. The number of intra- and inter-modular connections was calculated to explain the PC changes. Additionally, we examined the potential associations of the measures mentioned above with clinical variables within the BN group.

RESULTS

Compared with the HC group, the BN group showed significantly decreased PC in the fronto-parietal network (FPN), cingulo-opercular network (CON), and cerebellum (Cere). Additionally, the number of intra-modular connections of the default mode network (DMN) and the number of the inter-modular connections between the DMN and CON, FPN and Cere, and CON and Cere in the BN group were lower than those in the HC group. The nodal level analysis showed that the BN group had a decreased PC of the anterior prefrontal cortex (aPFC), dorsal frontal cortex (dFC), inferior parietal lobule (IPL), thalamus, and angular gyrus. Further, these metrics were significantly correlated with clinical variables in the BN group.

DISCUSSION

These findings may provide novel insights to capture atypical topologies associated with pathophysiology mechanisms and clinical symptoms underlying BN.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

The effect of Bafa Wubu of Tai Chi on college students' anxiety and depression: A randomized, controlled pilot study

Objective: This pilot study aimed to explore the mechanism of the effects of Bafa Wubu of Tai Chi (BWTC) on anxiety and depression in college students using resting-state functional magnetic resonance imaging (RS-fMRI). Methods: Eighteen college students (5 males and 13 females) with anxiety and depression met the study criteria and were randomly divided into an experimental group (aged 24.20 ± 4.07 years) and a control group (aged 22.50 ± 5.95). The experimental group received an eight-week BWTC intervention five times/week for 60 min/session. The control group maintained normal daily life without any exercise intervention. These students were assessed using RS-fMRI scans, the self-rating anxiety scale (SAS), and the self-rating depression scale (SDS). Spearman correlation analysis was used, and statistical significance was defined as a two-sided p-value of <0.05. Results: After the intervention, the SAS and SDS scores of the BWTC group significantly reduced (p = 0.002; p = 0.001). Compared with the control group, the fALFF values of the right middle frontal gyrus, orbital part (Frontal_Mid_Orb_R) (p = 0.043), right inferior occipital gyrus (Occipital_Inf_R) (p = 0.003), and right middle temporal gyrus of the temporal pole (Temporal_Pole_Mid_R) (p = 0.003) in the BWTC group increased significantly; the fALFF values of the left middle frontal gyrus (Frontal_Mid_L) (p = 0.001) and right supplementary motor area (Supp_Motor_Area_R) (p = 0.010) in BWTC group decreased significantly. The fALFF values of Frontal_Mid_Orb_R were significantly positively correlated with the SDS score (r = 0.852, p = 0.015) and the fALFF values of Frontal_Mid_L were significantly negatively correlated with the SAS score (r = -0.797, p = 0.032). Conclusion: In this pilot study with college students, BWTC alleviated anxiety and depression, potentially through modulating activity in the Frontal_Mid_L and Frontal_Mid_Orb_R, respectively.

Oxytocin-Receptor Gene Modulates Reward-Network Connection and Relationship with Empathy Performance

Introduction

Empathy traits are highly heritable and linked with reward processing. It is implicated that common variations of the oxytocin-receptor gene (OXTR) play a modulatory effect on empathic performance. However, it is unclear about the neural substrates underlying the modulatory effect of the OXTR genotype on empathic performance. This study aimed to characterize the modulatory effect of common OXTR variations on reward-circuitry function and its relationship with empathy.

Methods

Based on the seed of the nucleus accumbens (NAcc; a key hub of reward circuitry), we examined differences in spontaneous local activity and functional connectivity between OXTR rs2268493 genotype groups and their relationship with empathic performance among 402 high-homogeneity participants.

Results

Comparing with C carriers (CC/CT) group, the individuals with the rs2268493 TT genotype exhibited lower functional connectivity of the right NAcc with the medial prefrontal cortex (mPFC) and inferior frontal gyrus. Similarly lower functional connectivity was found between the left NAcc and mPFC. Consequently, no significant difference was found in the spontaneous local activity of NAcc.

Discussion

Our findings suggested that common OXTR variations have a modulatory effect on the connection of the NAcc with the hub of empathic networks (mPFC and IFG), which may provide insight on the neural substrate underlying the modulatory effect of OXTR on empathic behavior.

A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis

Autism spectrum disorder (ASD) is a lifelong neurological disease, which seriously reduces the patients' life quality. Generally, an early diagnosis is beneficial to improve ASD children's life quality. Current methods based on samples from multiple sites for ASD diagnosis perform poorly in generalization due to the heterogeneity of the data from multiple sites. To address this problem, this paper presents a similarity measure-based approach for ASD diagnosis. Specifically, the few-shot learning strategy is used to measure potential similarities in the RS-fMRI data distributions, and, furthermore, a similarity function for samples from multiple sites is trained to enhance the generalization. On the ABIDE database, the presented approach is compared to some representative methods, such as SVM and random forest, in terms of accuracy, precision, and F1 score. The experimental results show that the experimental indicators of the proposed method are better than those of the comparison methods to varying degrees. For example, the accuracy on the TRINITY site is more than 5% higher than that of the comparison method, which clearly proves that the presented approach achieves a better generalization performance than the compared methods.

Altered Spontaneous Brain Activity Patterns of Meibomian Gland Dysfunction in Severely Obese Population Measured Using the Fractional Amplitude of Low-Frequency Fluctuations

OBJECTIVE

Utilizing the fractional amplitude of low-frequency fluctuations (fALFF) technique, this study sought to correlate spontaneous cerebral abnormalities with the clinical manifestations of meibomian gland dysfunction (MGD) in severely obese (SO) population.

SUBJECTS AND METHODS

Twelve MGD patients in SO population (PATs) (4 males and 8 females) and twelve healthy controls (HCs) (6 males and 6 females) matched by gender and age were enrolled. Every participant underwent resting-state functional magnetic resonance imaging (rs-MRI) scanning. Spontaneous cerebral activity alterations were examined using the fALFF method. Receiver operating characteristic (ROC) curves were utilized to classify the medial fALFF values of the PATs and HCs. PATs were also asked to complete anxiety and depression score forms, permitting a correlation analysis.

RESULTS

In contrast with HCs, PATs had prominently increased fALFF values in the left lingual gyrus, the right globus pallidus, the right anterior cingulate and paracingulate gyri and the left middle occipital lobe (P < 0.05), and decreased fALFF values in the right cerebellum, the left fusiform gyrus, the right medial orbitofrontal gyrus, the left triangle inferior frontal gyrus and the left inferior parietal gyrus (P < 0.05). The results of the ROC curve indicated that changes in regional fALFF values might help diagnose MGD in SO population. Moreover, fALFF values in the right cerebellum of PATs were positively correlated with hospital anxiety and depression scores (HADS) (r = 0.723, P = 0.008). The fALFF values in the left triangle inferior frontal gyrus of PAT were negatively correlated with HADS (r = -0.651, P = 0.022).

CONCLUSIONS

Aberrant spontaneous activity was observed in multiple regions of the cerebrum, offering helpful information about the pathology of MGD in SO population. Aberrant fALFF values in these regions likely relates to the latent pathologic mechanisms of anomalous cerebral activities in PATs.

Resting-State fMRI Functional Connectivity Strength Predicts Local Activity Change in the Dorsal Cingulate Cortex: A Multi-Target Focused rTMS Study

Previous resting state functional magnetic resonance imaging (RS-fMRI) studies suggested that repetitive transcranial magnetic stimulation (rTMS) can modulate local activity in distant areas via functional connectivity (FC). A brain region has more than one connection with the superficial cortical areas. The current study proposed a multi-target focused rTMS protocol for indirectly stimulating a deep region, and to investigate 1) whether FC strength between stimulation targets (right middle frontal gyrus [rMFG] and right inferior parietal lobule [rIPL]) and effective region (dorsal anterior cingulate cortex [dACC]) can predict local activity changes of dACC and 2) whether multiple stimulation targets can focus on the dACC via FC. A total of 24 healthy participants received rTMS with two stimulation targets, both showing strong FC with the dACC. There were four rTMS conditions (>1 week apart, 10 Hz, 1800 pulses for each): rMFG-target, rIPL-target, Double-targets (900 pulses for each target), and Sham. The results failed to validate the multi-target focused rTMS hypothesis. But rMFG-target significantly decreased the local activity in the dACC. In addition, stronger dACC-rMFG FC was associated with a greater local activity change in the dACC. Future studies should use stronger FC to focus stimulation effects on the deep region.

Altered functional connectivity in adolescent anorexia nervosa is related to age and cortical thickness

INTRODUCTION

Functional networks develop throughout adolescence when anorexia nervosa (AN) normally debuts. In AN, cerebral structural alterations are found in most brain regions and may be related to the observed functional brain changes. Few studies have investigated the functional networks of the brain in adolescent AN patients.. The aim of this explorative study was to investigate multiple functional networks in adolescent AN patients compared to healthy age-matched controls (HC) and the relationship with age, eating disorder symptoms and structural alterations.

METHODS

Included were 29 female inpatients with restrictive AN, and 27 HC. All participants were between the ages of 12 to 18 years. Independent component analysis (ICA) identified 21 functional networks that were analyzed with multivariate and univariate analyses of components and group affiliation (AN vs HC). Age, age × group interaction and AN symptoms were included as covariates. Follow-up correlational analyses of selected components and structural measures (cortical thickness and subcortical volume) were carried out.

RESULTS

Decreased functional connectivity (FC) in AN patients was found in one cortical network, involving mainly the precuneus, and identified as a default mode network (DMN). Cortical thickness in the precuneus was significantly correlated with functional connectivity in this network. Significant group differences were also found in two subcortical networks involving mainly the hippocampus and the amygdala respectively, and a significant interaction effect of age and group was found in both these networks. There were no significant associations between FC and the clinical measures used in the study.

CONCLUSION

The findings from the present study may imply that functional alterations are related to structural alterations in selected regions and that the restricted food intake in AN patients disrupt normal age-related development of functional networks involving the amygdala and hippocampus.

Modulatory effects of magnetic vestibular stimulation on resting-state networks can be explained by subject-specific orientation of inner-ear anatomy in the MR static magnetic field

Strong static magnetic fields, as used in magnetic resonance imaging (MRI), stimulate the vestibular inner ear leading to a state of imbalance within the vestibular system that causes nystagmus. This magnetic vestibular stimulation (MVS) also modulates fluctuations of resting-state functional MRI (RS-fMRI) networks. MVS can be explained by a Lorentz force model, indicating that MVS is the result of the interaction of the static magnetic field strength and direction (called "B0 magnetic field" in MRI) with the inner ear's continuous endolymphatic ionic current. However, the high variability between subjects receiving MVS (measured as nystagmus slow-phase velocity and RS-fMRI amplitude modulations) despite matching head position, remains to be explained. Furthermore, within the imaging community, an "easy-to-acquire-and-use" proxy accounting for modulatory MVS effects in RS-fMRI fluctuations is needed. The present study uses MRI data of 60 healthy volunteers to examine the relationship between RS-fMRI fluctuations and the individual orientation of inner-ear anatomy within the static magnetic field of the MRI. The individual inner-ear anatomy and orientation were assessed via high-resolution anatomical CISS images and related to fluctuations of RS-fMRI networks previously associated with MVS. More specifically, we used a subject-specific proxy for MVS (pMVS) that corresponds to the orientation of the individual inner-ear anatomy within the static magnetic field direction (also called "z-direction" in MR imaging). We found that pMVS explained a considerable fraction of the total variance in RS-fMRI fluctuations (for instance, from 11% in the right cerebellum up to 36% in the cerebellar vermis). In addition to pMVS, we examined the angle of Reid's plane, as determined from anatomical imaging as an alternative and found that this angle (with the same sinus transformation as for pMVS) explained considerably less variance, e.g., from 2 to 16%. In our opinion, an excess variability due to MVS should generally be addressed in fMRI research analogous to nuisance regression for movement, pulsation, and respiration effects. We suggest using the pMVS parameter to deal with modulations of RS-fMRI fluctuations due to MVS. MVS-induced variance can easily be accounted by using high-resolution anatomical imaging of the inner ear and including the proposed pMVS parameter in fMRI group-level analysis.

Disrupted interhemispheric coordination with unaffected lateralization of global eigenvector centrality characterizes hemiparkinsonism

OBJECTIVE

The motor dysfunctions always affect hemi-body first in Parkinson's disease (PD). However, the interhemispheric relationships in patients with only unilateral motor impairment were barely known to date. We aimed to investigate the interhemispheric functions using resting-state functional Magnetic resonance imaging (RS-fMRI) for further understanding the pathogenesis of PD.

METHODS

Forty-three unilateral-symptomatic PD patients (UPD, Hoehn-Yahr staging scale, H-Y: 1-1.5), and 54 age-, gender-, education-matched normal controls (NC) were recruited. All subjects underwent MRI scanning and clinical evaluations. The interhemispheric coordination (Voxel-Mirrored Homotopic Connectivity, VMHC) and hemispheric dominance pattern (laterality index of eigenvector centrality mapping, LI-ECM) were calculated. Afterwards, correlation analyses and receiver operating characteristic (ROC) curve analysis were employed.

RESULTS

Compared with NC, UPD group showed significantly decreased VMHC in bilateral sensorimotor regions which was negatively correlated with the motor score. Furthermore, at the cut-off homotopic connectivity of 0.604, statistically significant ability of VMHC to discriminate UPD from NC with area under ROC curve (AUC) = 0.759, p < 0.001; specificity = 74.4%; sensitivity = 68.5% was observed. No difference was detected in UPD patients as for ECM and LI-ECM.

CONCLUSIONS

The disrupted interhemispheric coordination in bilateral sensorimotor regions may have significant implications for elucidating the mechanisms underlying the hemiparkinsonism and enabling the uncovering of complex mechanisms of PD.

Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 min. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. After validating the method on a synthetic dataset, we analyze data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

HIV infection across aging: Synergistic effects on intrinsic functional connectivity of the brain

The objective of the study was to examine additive and synergistic effects of age and HIV infection on resting state (RS) intra- and inter-network functional connectivity (FC) of the brain. We also aimed to assess relationships with neurocognition and determine clinical-, treatment-, and health-related factors moderating intrinsic brain activity in aging HIV-positive (HIV+) individuals. The current report presents data on 54 HIV+ individuals (age M = 41, SD = 12 years) stabilized on cART and 54 socio-demographically matched healthy (HIV-) comparators (age M = 43, SD = 12 years), with cohort education mean of 16 years (SD = 12). Age at seroconversion ranged 20-55 years old. ANOVA assessed additive and synergistic effects of age and HIV in 133 ROIs. Bivariate statistics examined relationships of FC indices vulnerable to age-HIV interactions and neurocognitive domains T-scores (attention, executive, memory, psychomotor, semantic skills). Multivariate logistic models determined covariates of FC. This study found no statistically significant age-HIV effects on RS-FC after correcting for multiple comparisons except for synergistic effects on connectivity within cingulo-opercular network (CON) at the trending level. However, for uncorrected RS connectivity analyses, we observed HIV-related strengthening between regions of fronto-parietal network (FPN) and default mode network (DMN), and particular DMN regions and sensorimotor network (SMN). Simultaneously, FC weakening was observed within FPN and between other regions of DMN-SMN, in HIV+ vs. HIV- individuals. Ten ROI pairs revealed age-HIV interactions, with FC decreasing with age in HIV+, while increasing in controls. FC correlated with particular cognitive domains positively in HIV+ vs. negatively in HIV- group. Proportion of life prior-to-after HIV-seroconversion, post-infection years, and treatment determined within-FPN and SMN-DMN FC. In sum, highly functioning HIV+/cART+ patients do not reveal significantly altered RS-FC from healthy comparators. Nonetheless, the current findings uncorrected for multiple comparisons suggest that HIV infection may lead to simultaneous increases and decreases in FC in distinct brain regions even in patients successfully stabilized on cART. Moreover, RS-fMRI ROI-based analysis can be sensitive to age-HIV interactions, which are especially pronounced for inter-network FC in relation to neurocognition. Aging and treatment-related factors partially explain RS-FC in aging HIV+ patients.

Snack food as a modulator of human resting-state functional connectivity

OBJECTIVE

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation.

METHODS

Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques.

RESULTS

Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus.

CONCLUSION

The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

The Connectivity Fingerprint of the Human Frontal Cortex, Subthalamic Nucleus, and Striatum

Within the cortico basal ganglia (BG)-thalamic network, the direct and indirect pathways comprise of projections from the cortex to the striatum (STR), whereas the hyperdirect pathway(s) consist of cortical projections toward the subthalamic nucleus (STN). Each pathway possesses a functionally distinct role for action selection. The current study quantified and compared the structural connectivity between 17 distinct cortical areas with the STN and STR using 7 Tesla diffusion weighted magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) in healthy young subjects. The selection of these cortical areas was based on a literature search focusing on animal tracer studies. The results indicate that, relative to other cortical areas, both the STN and STR showed markedly weaker structural connections to areas assumed to be essential for action inhibition such as the inferior frontal cortex pars opercularis. Additionally, the cortical connectivity fingerprint of the STN and STR indicated relatively strong connections to areas related to voluntary motor initiation such as the cingulate motor area and supplementary motor area. Overall the results indicated that the cortical-STN connections were sparser compared to the STR. There were two notable exceptions, namely for the orbitofrontal cortex and ventral medial prefrontal cortex, where a higher tract strength was found for the STN. These two areas are thought to be involved in reward processing and action bias.

Total Salvianolic Acid Balances Brain Functional Network Topology in Rat Hippocampi Overexpressing miR-30e

We investigated the therapeutic effects and underlying brain functional network topology mechanisms of total salvianolic acid (TSA) treatment for memory dysfunction by using miR-30e overexpression-induced memory deficit in rat hippocampi. Model rats were developed by lentivirus vectors carrying miR-30e into bilateral hippocampus CA1 region through stereo-surgery. Two weeks after surgery, TSA (20 or 10 mg/mL/kg) or saline were administrated for 14 consecutive days. Memory function was assessed by behavioral tests (Y maze and Morris water maze [MWM]); resting-state functional MRI (RS-fMRI); and molecular alterations of BCL-2, UBC9, and Caspase-3 in the hippocampus CA1 region, as detected by immunohistochemistry. Compared to controls, model rats exhibited significantly impaired working and long-term memory in the Y maze and MWM tests (p < 0.01). The brain functional network topology analyzed based on RS-fMRI data demonstrated that miR-30e disturbed the global integration and segregation balance of the brain (p < 0.01), and reduced edge strength between CA1 and the posterior cingulate, temporal lobe, and thalamus (p < 0.05, false discovery rate corrected). At the molecular level, BCL-2 and UBC9 were downregulated, while Caspase-3 was upregulated (p < 0.01). After TSA (20 mg/mL/kg) treatment, the biomarkers for behavioral performance, global integration and segregation, edge strength, and expression levels of BCL-2, UBC9, and Caspase3 returned to normal levels. The correlation analyses of these results showed that global brain functional network topologic parameters can be intermediate biomarkers correlated with both behavioral changes and molecular alterations. This indicated that the effects of TSA were achieved by inhibiting apoptosis of CA1 neurons to improve global functional network topology.

Defective functional connectivity between posterior hypothalamus and regions of the diencephalic-mesencephalic junction in chronic cluster headache

OBJECTIVE

We tested the hypothesis of a defective functional connectivity between the posterior hypothalamus and diencephalic-mesencephalic regions in chronic cluster headache based on: a) clinical and neuro-endocrinological findings in cluster headache patients; b) neuroimaging findings during cluster headache attacks; c) neuroimaging findings in drug-refractory chronic cluster headache patients improved after successful deep brain stimulation.

METHODS

Resting state functional magnetic resonance imaging, associated with a seed-based approach, was employed to investigate the functional connectivity of the posterior hypothalamus in chronic cluster headache patients (n = 17) compared to age and sex-matched healthy subjects (n = 16). Random-effect analyses were performed to study differences between patients and controls in ipsilateral and contralateral-to-the-pain posterior hypothalamus functional connectivity.

RESULTS

Cluster headache patients showed an increased functional connectivity between the ipsilateral posterior hypothalamus and a number of diencephalic-mesencephalic structures, comprising ventral tegmental area, dorsal nuclei of raphe, and bilateral substantia nigra, sub-thalamic nucleus, and red nucleus ( p < 0.005 FDR-corrected vs . control group). No difference between patients and controls was found comparing the contralateral hypothalami.

CONCLUSIONS

The observed deranged functional connectivity between the posterior ipsilateral hypothalamus and diencephalic-mesencephalic regions in chronic cluster headache patients mainly involves structures that are part of (i.e. ventral tegmental area, substantia nigra) or modulate (dorsal nuclei of raphe, sub-thalamic nucleus) the midbrain dopaminergic systems. The midbrain dopaminergic systems could play a role in cluster headache pathophysiology and in particular in the chronicization process. Future studies are needed to better clarify if this finding is specific to cluster headache or if it represents an unspecific response to chronic pain.

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for autism spectrum disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.

Comparison of ApoE-related brain connectivity differences in early MCI and normal aging populations: an fMRI study

In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) scans from subjects with early mild cognitive impairment (EMCI) and control subjects to study functional network connectivity. The scans were acquired by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used genetic data from the ADNI database to further subdivide the EMCI and control groups into genotype groups with or without the Apolipoprotein E allele e4 (APOE e4). Region of interest (ROI)-to-ROI resting-state functional connectivity was measured using Freesurfer and the Functional Connectivity Toolbox for Matlab (CONN). In our analysis, we compared whole-brain ROI connectivity strength and ROI-to-ROI functional network connectivity strength between EMCI, control and genotype subject groups. We found that the ROI network properties were disrupted in EMCI and APOE e4 carrier groups. Notably, we show that (1) EMCI disrupts functional connectivity strength in many important functionally-linked areas; (2) APOE e4 disrupts functional connectivity strength in similar areas to EMCI; and (3) the differences in functional connectivity between groups shows a multifactor contribution to functional network dysfunction along the trajectory leading to dementia.

The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes

The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. These fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD). The AD represents a multifarious syndrome characterized by structural, functional, and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI) and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF) to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI) repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI) acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to track the individual progression of the impairments prospecting a proficient usage in a wide range of pathophysiological and physiological studies as well.

Progesterone mediates brain functional connectivity changes during the menstrual cycle-a pilot resting state MRI study

The growing interest in intrinsic brain organization has sparked various innovative approaches to generating comprehensive connectivity-based maps of the human brain. Prior reports point to a sexual dimorphism of the structural and functional human connectome. However, it is uncertain whether subtle changes in sex hormones, as occur during the monthly menstrual cycle, substantially impact the functional architecture of the female brain. Here, we performed eigenvector centrality (EC) mapping in 32 longitudinal resting state fMRI scans of a single healthy subject without oral contraceptive use, across four menstrual cycles, and assessed estrogen and progesterone levels. To investigate associations between cycle-dependent hormones and brain connectivity, we performed correlation analyses between the EC maps and the respective hormone levels. On the whole brain level, we found a significant positive correlation between progesterone and EC in the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral sensorimotor cortex. In a secondary region-of-interest analysis, we detected a progesterone-modulated increase in functional connectivity of both bilateral DLPFC and bilateral sensorimotor cortex with the hippocampus. Our results suggest that the menstrual cycle substantially impacts intrinsic functional connectivity, particularly in brain areas associated with contextual memory-regulation, such as the hippocampus. These findings are the first to link the subtle hormonal fluctuations that occur during the menstrual cycle, to significant changes in regional functional connectivity in the hippocampus in a longitudinal design, given the limitation of data acquisition in a single subject. Our study demonstrates the feasibility of such a longitudinal Resting-state functional Magnetic Resonance Imaging (rs-fMRI) design and illustrates a means of creating a personalized map of the human brain by integrating potential mediators of brain states, such as menstrual cycle phase.

Cortical connective field estimates from resting state fMRI activity

One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that—despite some variability in CF estimates between RS scans—neural properties such as CF maps and CF size can be derived from resting state data.

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG–fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake–sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.

Functional overlap and divergence between ALS and bvFTD

Amyotrophic lateral sclerosis (ALS) and behavioral variant frontotemporal dementia (bvFTD) lie on a clinical, pathologic, and genetic continuum. Neuroimaging techniques have proven to be potentially useful to unravel the shared features of these syndromes. Using resting-state functional magnetic resonance imaging (RS-fMRI), we investigated functional connectivity of brain networks in 15 ALS and 15 bvFTD patients in early stages of disease and 15 healthy controls, looking expressly for connectivity pattern divergence or overlap between the 2 disorders. Compared with controls, we found decreased RS-fMRI signals within sensorimotor, right frontoparietal, salience, and executive networks in both patient groups. Within the default mode network (DMN), divergent connectivity patterns were observed, with RS-fMRI signals in the posterior cingulate cortex enhanced in bvFTD patients and suppressed in ALS patients. Our findings confirm that ALS and bvFTD not only broadly share common RS-fMRI connectivity patterns, probably representing different phenotypical expressions of the same neurodegenerative process, but also differ in the DMN, probably reflecting a different stage of neurodegeneration.