Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

Large-scale brain network activation during emotional inhibitory control: Associations with alcohol misuse in college freshmen.

BACKGROUND: The transition to college is associated with increased risk of alcohol misuse and a consequent increase in negative, alcohol-related social and health impacts. Traits associated with ongoing brain maturation during this period, including impulsivity in emotional contexts, could contribute to risky alcohol use. METHODS: This functional magnetic resonance imaging (fMRI) study examined brain network activation strength during an emotional inhibitory control task (Go-NoGo), which required participants to ignore background images with negative or neutral emotional valence during performance. Participants were 60 college freshmen (aged 18-20 years, 33 women). Survey measures, completed at baseline and one-year follow-up (follow-up n = 52, 29 women), assessed alcohol misuse alcohol use disorders identification test (AUDIT), alcohol/substance use counseling center assessment of psychological symptoms (C-CAPS), and negative consequences of alcohol use young adult alcohol consequences questionnaire (YAACQ). Measures were examined relative to network activation strength, on the Negative NoGo > Neutral NoGo contrast, of four large-scale brain networks implicated in top-down regulation of cognition and attention: right and left lateral frontoparietal networks (rL-FPN; lL-FPN), dorsal attention network (DAN), and salience network (SN). RESULTS: Activation strength of DAN was negatively associated with scores on the AUDIT (p = 0.013) and YAACQ (p = 0.004) at baseline, and with C-CAPS score at baseline and follow-up (p = 0.002; p = 0.005), and positively associated with accuracy on NoGo trials with negative backgrounds (p = 0.014). Activation strength of rL-FPN was positively associated with C-CAPS score at follow-up (p = 0.003). SN activation strength was negatively associated with accuracy on NoGo trials with negative (p < 0.001) and neutral (p = 0.002) backgrounds and with the accuracy difference between negative versus neutral NoGo trials (p = 0.003). CONCLUSION: These findings suggest that less engagement of large-scale brain circuitry that supports top-down attentional control, specifically during negative emotions, is associated with more problematic drinking in emerging adults who attend college. This pattern of network activation may serve as a risk marker for ongoing self-regulation deficits during negative emotion that could increase risk of problematic alcohol use and negative impacts of drinking.

Four-Week Mentalizing Imagery Therapy for Family Dementia Caregivers: A Randomized Controlled Trial with Neural Circuit Changes.

INTRODUCTION: Family caregivers of patients with dementia suffer a high burden of depression and reduced positive emotions. Mentalizing imagery therapy (MIT) provides mindfulness and guided imagery skills training to improve balanced mentalizing and emotion regulation. OBJECTIVE: Our aims were to test the hypotheses that MIT for family caregivers would reduce depression symptoms and improve positive psychological traits more than a support group (SG), and would increase dorsolateral prefrontal cortex (DLPFC) connectivity and reduce subgenual anterior cingulate cortex (sgACC) connectivity. METHODS: Forty-six caregivers participated in a randomized controlled trial comparing a 4-week MIT group (n = 24) versus an SG (n = 22). Resting state neuroimaging was obtained at baseline and post-group in 28 caregivers, and questionnaires completed by all participants. The primary outcome was change in depression; secondary measures included anxiety, mindfulness, self-compassion, and well-being. Brain networks with participation of DLPFC and sgACC were identified. Connectivity strengths of DLPFC and sgACC with respective networks were determined with dual regression. DLPFC connectivity was correlated with mindfulness and depression outcomes. RESULTS: MIT significantly outperformed SG in improving depression, anxiety, mindfulness, self-compassion, and well-being, with moderate to large effect sizes. Relative to SG, participants in MIT showed significant increases in DLPFC connectivity - exactly replicating pilot study results - but no change in sgACC. DLPFC connectivity change correlated positively with mindfulness and negatively with depression change. CONCLUSIONS: In this trial, MIT was superior to SG for reducing depression and anxiety symptoms and improving positive psychological traits. Neuroimaging results suggested that strengthening DLPFC connectivity with an emotion regulation network might be mechanistically related to MIT effects.

Denoising scanner effects from multimodal MRI data using linked independent component analysis.

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

Morphometric Biomarkers of Adolescents With Familial Risk for Alcohol Use Disorder.

BACKGROUND: While many adolescents exhibit risky behavior, teenagers with a family history (FH+) of an alcohol use disorder (AUD) are at a heightened risk for earlier initiation of alcohol use, a more rapid escalation in frequency and quantity of alcohol consumption and developing a subsequent AUD in comparison with youth without such family history (FH-). Neuroanatomically, developmentally normative risk-taking behavior parallels an imbalance between more protracted development of the prefrontal cortex (PFC) and earlier development of limbic regions. Magnetic resonance imaging (MRI)-derived volumetric properties were obtained for these structures in FH+ and FH- adolescents. METHODS: Forty-two substance-naïve adolescents (13- to 14-year-olds), stratified into FH+ (N = 19, 13 girls) and FH- (N = 23, 11 girls) age/handedness-matched groups, completed MRI scanning at 3.0T, as well as cognitive and clinical testing. T1 images were processed using FreeSurfer to measure PFC and hippocampi/amygdalae subfields/nuclei volumes. RESULTS: FH+ status was associated with larger hippocampal/amygdala volumes (p < 0.05), relative to FH- adolescents, with right amygdala results appearing to be driven by FH+ boys. Volumetric differences also were positively associated with family history density (p < 0.05) of having an AUD. Larger subfields/nuclei volumes were associated with higher anxiety levels and worse auditory verbal learning performance (p < 0.05). CONCLUSIONS: FH+ risk for AUD is detectable via neuromorphometric characteristics, which precede alcohol use onset and the potential onset of a later AUD, that are associated with emotional and cognitive measures. It is plausible that the development of limbic regions might be altered in FH+ youth, even prior to the onset of alcohol use, which could increase later risk. Thus, targeted preventative measures are warranted that serve to delay the onset of alcohol use in youth, particularly in those who are FH+ for an AUD.

Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.

Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non-stationary voxelation-corrected cluster-size test (CST) based on random field theory and threshold-free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group-level fMRI analysis compared to other statistical methods. In this work, the non-stationary voxelation-corrected CST and TFCE methods for group-level analysis were evaluated for both stationary and non-stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel-wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group-level analysis of VBM data. Thus, the voxelation-corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single-subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 38:1269-1280, 2017. © 2016 Wiley Periodicals, Inc.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry.

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Disruption of white matter structural integrity and connectivity in posttraumatic stress disorder: A TBSS and tractography study.

BACKGROUND: Most studies of brain white matter (WM) in posttraumatic stress disorder (PTSD) have focused on combat trauma, and often were confounded by neurological and substance dependence comorbidity. This study used tract-based spatial statistics (TBSS) and probabilistic tractography to characterize WM microstructure in a mixed-sex community sample of PTSD patients exposed to diverse and multiple traumas, and in trauma-exposed normal comparison (TENC) subjects. METHODS: TBSS compared diffusion measures between 20 adults with DSM-IV PTSD and 17 TENC, using a whole-brain voxel-wise approach. Probabilistic tractography using Freesurfer's TRACULA was employed to measure diffusion tensor imaging (DTI) metrics within anatomically defined pathways. DTI metrics were compared between groups and correlated with PTSD symptom severity and trauma load. RESULTS: Controlling for age, sex, and motion, PTSD subjects had significantly reduced fractional anisotropy (FA) in a left frontal lobe cluster compared with TENC, at p < .05, family-wise error corrected. Tractography identified significant group differences in the inferior longitudinal fasciculus (ILF), including lower FA and higher radial diffusivity in PTSD compared with TENC. Within the PTSD group, FA values were not correlated with symptom severity or trauma load. Results remained significant after removing participants using psychotropic medication or those with comorbid major depression. CONCLUSIONS: PTSD patients had reduced WM integrity in left hemisphere frontal WM and temporal-occipital WM tracts, compared to trauma-exposed controls. Reduced frontal FA is consistent with compromised top-down attentional control and emotion regulation in PTSD, while reduced ILF FA may be related to sensory processing and gating abnormalities in this disorder.

Functional Connectivity of Insula, Basal Ganglia, and Prefrontal Executive Control Networks during Hypoglycemia in Type 1 Diabetes.

Human brain networks mediating interoceptive, behavioral, and cognitive aspects of glycemic control are not well studied. Using group independent component analysis with dual-regression approach of functional magnetic resonance imaging data, we examined the functional connectivity changes of large-scale resting state networks during sequential euglycemic-hypoglycemic clamp studies in patients with type 1 diabetes and nondiabetic controls and how these changes during hypoglycemia were related to symptoms of hypoglycemia awareness and to concurrent glycosylated hemoglobin (HbA1c) levels. During hypoglycemia, diabetic patients showed increased functional connectivity of the right anterior insula and the prefrontal cortex within the executive control network, which was associated with higher HbA1c. Controls showed decreased functional connectivity of the right anterior insula with the cerebellum/basal ganglia network and of temporal regions within the temporal pole network and increased functional connectivity in the default mode and sensorimotor networks. Functional connectivity reductions in the right basal ganglia were correlated with increases of self-reported hypoglycemic symptoms in controls but not in patients. Resting state networks that showed different group functional connectivity during hypoglycemia may be most sensitive to glycemic environment, and their connectivity patterns may have adapted to repeated glycemic excursions present in type 1 diabetes. Our results suggest that basal ganglia and insula mediation of interoceptive awareness during hypoglycemia is altered in type 1 diabetes. These changes could be neuroplastic adaptations to frequent hypoglycemic experiences. Functional connectivity changes in the insula and prefrontal cognitive networks could also reflect an adaptation to changes in brain metabolic pathways associated with chronic hyperglycemia. SIGNIFICANCE STATEMENT: The major factor limiting improved glucose control in type 1 diabetes is the significant increase in hypoglycemia associated with insulin treatment. Repeated exposure to hypoglycemia alters patients' ability to recognize the autonomic and neuroglycopenic symptoms associated with low plasma glucose levels. We examined brain resting state networks during the induction of hypoglycemia in diabetic and control subjects and found differences in networks involved in sensorimotor function, cognition, and interoceptive awareness that were related to chronic levels of glycemic control. These findings identify brain regions that are sensitive to variations in plasma glucose levels and may also provide a basis for understanding the mechanisms underlying the increased incidence of cognitive impairment and affective disorders seen in patients with diabetes.

A voxelation-corrected non-stationary 3D cluster-size test based on random field theory.

Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom.

Memory retrieval of smoking-related images induce greater insula activation as revealed by an fMRI-based delayed matching to sample task.

Nicotine dependence is a chronic and difficult to treat disorder. While environmental stimuli associated with smoking precipitate craving and relapse, it is unknown whether smoking cues are cognitively processed differently than neutral stimuli. To evaluate working memory differences between smoking-related and neutral stimuli, we conducted a delay-match-to-sample (DMS) task concurrently with functional magnetic resonance imaging (fMRI) in nicotine-dependent participants. The DMS task evaluates brain activation during the encoding, maintenance and retrieval phases of working memory. Smoking images induced significantly more subjective craving, and greater midline cortical activation during encoding in comparison to neutral stimuli that were similar in content yet lacked a smoking component. The insula, which is involved in maintaining nicotine dependence, was active during the successful retrieval of previously viewed smoking versus neutral images. In contrast, neutral images required more prefrontal cortex-mediated active maintenance during the maintenance period. These findings indicate that distinct brain regions are involved in the different phases of working memory for smoking-related versus neutral images. Importantly, the results implicate the insula in the retrieval of smoking-related stimuli, which is relevant given the insula's emerging role in addiction.

A high performance 3D cluster-based test of unsmoothed fMRI data.

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.

Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry.

The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness.

Association of Lower Rostral Anterior Cingulate GABA+ and Dysregulated Cortisol Stress Response With Altered Functional Connectivity in Young Adults With Lifetime Depression: A Multimodal Imaging Investigation of Trait and State Effects.

OBJECTIVE: Preclinical work suggests that excess glucocorticoids and reduced cortical γ-aminobutyric acid (GABA) may affect sex-dependent differences in brain regions implicated in stress regulation and depressive phenotypes. The authors sought to address a critical gap in knowledge, namely, how stress circuitry is functionally affected by glucocorticoids and GABA in current or remitted major depressive disorder (MDD). METHODS: Multimodal imaging data were collected from 130 young adults (ages 18-25), of whom 44 had current MDD, 42 had remitted MDD, and 44 were healthy comparison subjects. GABA+ (γ-aminobutyric acid and macromolecules) was assessed using magnetic resonance spectroscopy, and task-related functional MRI data were collected under acute stress and analyzed using data-driven network modeling. RESULTS: Across modalities, trait-related abnormalities emerged. Relative to healthy comparison subjects, both clinical groups were characterized by lower rostral anterior cingulate cortex (rACC) GABA+ and frontoparietal network amplitude but higher amplitude in salience and stress-related networks. For the remitted MDD group, differences from the healthy comparison group emerged in the context of elevated cortisol levels, whereas the MDD group had lower cortisol levels than the healthy comparison group. In the comparison group, frontoparietal and stress-related network connectivity was positively associated with cortisol level (highlighting putative top-down regulation of stress), but the opposite relationship emerged in the MDD and remitted MDD groups. Finally, rACC GABA+ was associated with stress-induced changes in connectivity between overlapping default mode and salience networks. CONCLUSIONS: Lifetime MDD was characterized by reduced rACC GABA+ as well as dysregulated cortisol-related interactions between top-down control (frontoparietal) and threat (task-related) networks. These findings warrant further investigation of the role of GABA in the vulnerability to and treatment of MDD.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results.

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as default or visual. Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant's fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain.

The hypnotic zolpidem increases the synchrony of BOLD signal fluctuations in widespread brain networks during a resting paradigm.

Networks of brain regions having synchronized fluctuations of the blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) time-series at rest, or "resting state networks" (RSNs), are emerging as a basis for understanding intrinsic brain activity. RSNs are topographically consistent with activity-related networks subserving sensory, motor, and cognitive processes, and studying their spontaneous fluctuations following acute drug challenge may provide a way to understand better the neuroanatomical substrates of drug action. The present within-subject double-blind study used BOLD fMRI at 3T to investigate the functional networks influenced by the non-benzodiazepine hypnotic zolpidem (Ambien). Zolpidem is a positive modulator of γ-aminobutyric acid(A) (GABA(A)) receptors, and engenders sedative effects that may be explained in part by how it modulates intrinsic brain activity. Healthy participants (n=12) underwent fMRI scanning 45 min after acute oral administration of zolpidem (0, 5, 10, or 20mg), and changes in BOLD signal were measured while participants gazed at a static fixation point (i.e., at rest). Data were analyzed using group independent component analysis (ICA) with dual regression and results indicated that compared to placebo, the highest dose of zolpidem increased functional connectivity within a number of sensory, motor, and limbic networks. These results are consistent with previous studies showing an increase in functional connectivity at rest following administration of the positive GABA(A) receptor modulators midazolam and alcohol, and suggest that investigating how zolpidem modulates intrinsic brain activity may have implications for understanding the etiology of its powerful sedative effects.

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

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

Controversies and progress on standardization of large-scale brain network nomenclature.

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.