Publications by authors named "Vince D Calhoun"

747 Publications

Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.

Commun Biol 2021 Sep 14;4(1):1073. Epub 2021 Sep 14.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.
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http://dx.doi.org/10.1038/s42003-021-02592-2DOI Listing
September 2021

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

Neuroimage 2021 Sep 10:118552. Epub 2021 Sep 10.

Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Department of Pharmacology & Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA. Electronic address:

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

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

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

Discussion: These data suggest that the strength of spontaneous activity undergoes robust change during the transition from childhood to adolescence (i.e., puberty onset), with intriguing sex differences in some cortical areas. Future developmental studies should probe task-related oscillations and spontaneous activity in parallel.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118552DOI Listing
September 2021

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Neuroimage 2021 Aug 22;243:118502. Epub 2021 Aug 22.

Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118502DOI Listing
August 2021

Brain multimodal co-alterations related to delay discounting: a multimodal MRI fusion analysis in persons with and without cocaine use disorder.

BMC Neurosci 2021 Aug 20;22(1):51. Epub 2021 Aug 20.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Atlanta, GA, USA.

Background: Delay discounting has been proposed as a behavioral marker of substance use disorders. Innovative analytic approaches that integrate information from multiple neuroimaging modalities can provide new insights into the complex effects of drug use on the brain. This study implemented a supervised multimodal fusion approach to reveal neural networks associated with delay discounting that distinguish persons with and without cocaine use disorder (CUD).

Methods: Adults with (n = 35) and without (n = 37) CUD completed a magnetic resonance imaging (MRI) scan to acquire high-resolution anatomical, resting-state functional, and diffusion-weighted images. Pre-computed features from each data modality included whole-brain voxel-wise maps for gray matter volume, fractional anisotropy, and regional homogeneity, respectively. With delay discounting as the reference, multimodal canonical component analysis plus joint independent component analysis was used to identify co-alterations in brain structure and function.

Results: The sample was 58% male and 78% African-American. As expected, participants with CUD had higher delay discounting compared to those without CUD. One joint component was identified that correlated with delay discounting across all modalities, involving regions in the thalamus, dorsal striatum, frontopolar cortex, occipital lobe, and corpus callosum. The components were negatively correlated with delay discounting, such that weaker loadings were associated with higher discounting. The component loadings were lower in persons with CUD, meaning the component was expressed less strongly.

Conclusions: Our findings reveal structural and functional co-alterations linked to delay discounting, particularly in brain regions involved in reward salience, executive control, and visual attention and connecting white matter tracts. Importantly, these multimodal networks were weaker in persons with CUD, indicating less cognitive control that may contribute to impulsive behaviors.
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http://dx.doi.org/10.1186/s12868-021-00654-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377830PMC
August 2021

Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.

Gigascience 2021 Aug;10(8)

The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia.

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
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http://dx.doi.org/10.1093/gigascience/giab051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377301PMC
August 2021

Dynamic functional network connectivity associated with post-traumatic stress symptoms in COVID-19 survivors.

Neurobiol Stress 2021 Nov 5;15:100377. Epub 2021 Aug 5.

CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, 100101, China.

Accumulating evidence shows that Coronavirus Disease 19 (COVID-19) survivors may encounter prolonged mental issues, especially post-traumatic stress symptoms (PTSS). Despite manifesting a plethora of behavioral or mental issues in COVID-19 survivors, previous studies illustrated that static brain functional networks of these survivors remain intact. The insignificant results could be due to the conventional statistic network analysis was unable to reveal information that can vary considerably in different temporal scales. In contrast, time-varying characteristics of the dynamic functional networks may help reveal important brain abnormalities in COVID-19 survivors. To test this hypothesis, we assessed PTSS and collected functional magnetic resonance imaging (fMRI) with COVID-19 survivors discharged from hospitals and matched controls. Results showed that COVID-19 survivors self-reported a significantly higher PTSS than controls. Tapping into the moment-to-moment variations of the fMRI data, we captured the dynamic functional network connectivity (dFNC) states, and three discriminative reoccurring brain dFNC states were identified. First of all, COVID-19 survivors showed an increased occurrence of a dFNC state with heterogeneous patterns between sensorimotor and visual networks. More importantly, the occurrence rate of this state was significantly correlated with the severity of PTSS. Finally, COVID-19 survivors demonstrated decreased topological organizations in this dFNC state than controls, including the node strength, degree, and local efficiency of the supplementary motor area. To conclude, our findings revealed the altered temporal characteristics of functional networks and their associations with PTSS due to COVID- 19. The current results highlight the importance of evaluating dynamic functional network changes with COVID-19 survivors.
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http://dx.doi.org/10.1016/j.ynstr.2021.100377DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339567PMC
November 2021

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Sci Rep 2021 Aug 3;11(1):15746. Epub 2021 Aug 3.

Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.
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http://dx.doi.org/10.1038/s41598-021-95098-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8333350PMC
August 2021

Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder.

Alcohol Clin Exp Res 2021 Aug 2. Epub 2021 Aug 2.

The Mind Research Network, Albuquerque, New Mexico, USA.

Background: Fetal alcohol spectrum disorder (FASD) is a significant public health problem that is associated with a broad range of physical, neurocognitive, and behavioral effects resulting from prenatal alcohol exposure (PAE). Magnetic resonance imaging (MRI) has been an important tool for advancing our knowledge of abnormal brain structure and function in individuals with FASD. However, whereas only a small number of studies have applied graph theory-based network analysis to resting-state functional MRI (fMRI) data in individuals with FASD additional research in this area is needed.

Methods: Resting-state fMRI data were collected from adolescent and young adult participants (ages 12-22) with fetal alcohol syndrome (FAS) or alcohol-related neurodevelopmental disorder (ARND) and neurotypically developing controls (CNTRL) from previous studies. Group independent components analysis (gICA) was applied to fMRI data to extract components representing functional brain networks. Functional network connectivity (FNC), measured by Pearson correlation of the average independent component (IC) time series, was analyzed under a graph theory framework to compare network modularity, the average clustering coefficient, characteristic path length, and global efficiency between groups. Cognitive intelligence, measured by the Wechsler Abbreviated Scale of Intelligence (WASI), was compared and correlated to global network measures.

Results: Group comparisons revealed significant differences in the average clustering coefficient, characteristic path length, and global efficiency. Modularity was not significantly different between groups. The FAS and ARND groups scored significantly lower than the CNTRL group on Full Scale IQ (FS-IQ) and the Vocabulary subtest, but not the Matrix Reasoning subtest. No significant associations between intelligence and graph theory measures were detected.

Conclusion: Our results partially agree with previous studies examining global graph theory metrics in children and adolescents with FASD and suggest that the exposure to alcohol during prenatal development leads to disruptions in aspects of functional network segregation and integration.
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http://dx.doi.org/10.1111/acer.14673DOI Listing
August 2021

Frequency drift in MR spectroscopy at 3T.

Neuroimage 2021 Nov 24;241:118430. Epub 2021 Jul 24.

School of Health Sciences, Purdue University, West Lafayette, IN USA.

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.

Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).

Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.

Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118430DOI Listing
November 2021

Whole-brain Functional Connectivity Dynamics associated with Electroconvulsive Therapy Treatment Response.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Jul 22. Epub 2021 Jul 22.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States; Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States; Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States.

Background: Depressive episodes (DEP) characterized by abnormalities in cognitive functions and mood is a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthesia brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy.

Methods: In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis (ICA)-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy controls (HCs). The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis.

Results: Five reoccurring connectivity states were identified, and DEP had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. DEP patients changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared to HC. We further found that DEP patients had diminished global meta-state dynamism, two of which recovered to normal after ECT. Interestingly, the changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT.

Conclusions: These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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http://dx.doi.org/10.1016/j.bpsc.2021.07.004DOI Listing
July 2021

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder.

Front Hum Neurosci 2021 6;15:689488. Epub 2021 Jul 6.

Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 ( = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called "occupancy rate" or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected = 0.03). Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
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http://dx.doi.org/10.3389/fnhum.2021.689488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291148PMC
July 2021

DNA methylation under the major depression pathway predicts pediatric quality of life four-month post-pediatric mild traumatic brain injury.

Clin Epigenetics 2021 Jul 12;13(1):140. Epub 2021 Jul 12.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Place NE, 18th Floor, Atlanta, GA, 30303, USA.

Background: Major depression has been recognized as the most commonly diagnosed psychiatric complication of mild traumatic brain injury (mTBI). Moreover, major depression is associated with poor outcomes following mTBI; however, the underlying biological mechanisms of this are largely unknown. Recently, genomic and epigenetic factors have been increasingly implicated in the recovery following TBI.

Results: This study leveraged DNA methylation within the major depression pathway, along with demographic and behavior measures (features used in the clinical model) to predict post-concussive symptom burden and quality of life four-month post-injury in a cohort of 110 pediatric mTBI patients and 87 age-matched healthy controls. The results demonstrated that including DNA methylation markers in the major depression pathway improved the prediction accuracy for quality of life but not persistent post-concussive symptom burden. Specifically, the prediction accuracy (i.e., the correlation between the predicted value and observed value) of quality of life was improved from 0.59 (p = 1.20 × 10) (clinical model) to 0.71 (p = 3.89 × 10); the identified cytosine-phosphate-guanine sites were mainly in the open sea regions and the mapped genes were related to TBI in several molecular studies. Moreover, depression symptoms were a strong predictor (with large weights) for both post-concussive symptom burden and pediatric quality of life.

Conclusion: This study emphasized that both molecular and behavioral manifestations of depression symptoms played a prominent role in predicting the recovery process following pediatric mTBI, suggesting the urgent need to further study TBI-caused depression symptoms for better recovery outcome.
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http://dx.doi.org/10.1186/s13148-021-01128-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274037PMC
July 2021

Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis.

Front Neurosci 2021 18;15:682110. Epub 2021 Jun 18.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.

Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.
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http://dx.doi.org/10.3389/fnins.2021.682110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8250435PMC
June 2021

Frontoparietal network and neuropsychological measures in typically developing children.

Neuropsychologia 2021 08 10;159:107914. Epub 2021 Jun 10.

Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA. Electronic address:

Resting-state activity has been used to gain a broader understanding of typical and aberrant developmental changes. However, the developmental trajectory of resting-state activity in relation to cognitive performance has not been studied in detail. The present study assessed spectral characteristics of theta (5-8 Hz) and alpha (9-13 Hz) frequency bands during resting-state in a priori selected regions of the frontoparietal network (FPN). We also examined the relationship between resting-state activity and cognitive performance in typically developing children. We hypothesized that older children and children with high attentional scores would have higher parietal alpha activity and frontal theta activity while at rest compared to young children and those with lower attentional scores. MEG data were collected in 65 typically developing children, ages 9-14 years, as part of the Developmental Chronnecto-Genomics study. Resting-state data were collected during eyes open and eyes closed for 5 min. Participants completed the NIH Toolbox Flanker Inhibitory Control (FICA) and Attention Test and Dimensional Change Card Sort Test (DCCS) to assess top-down attentional control. Spectral power density was used to characterize the FPN. We found during eyes open and eyes closed, all participants had higher theta and alpha power in parietal regions relative to frontal regions. The group with high attentional scores had higher alpha power during resting-state eyes closed compared to those with low attentional scores. However, there were no significant differences between age groups, suggesting changes in the maturation of neural oscillations in theta and alpha are not evident among children in the 9-14-year age range.
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http://dx.doi.org/10.1016/j.neuropsychologia.2021.107914DOI Listing
August 2021

Lateralization of Resting-State Networks in Children: Association with Age, Sex, Handedness, Intelligence Quotient, and Behavior.

Brain Connect 2021 Jul 26. Epub 2021 Jul 26.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA.

Lateralization in brain function has been associated with age and sex in previous work; however, there has been less focus on lateralization of functional networks during development. We aim to examine laterality in typical development; a clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years. This is an extension of our previous work on normal aging in adults, where we now assess whether there are similar patterns in children. Unlike the results from our study of healthy aging in adults, which showed a decrease in laterality with increasing age, in this study we found both decreases and increases in lateralization in multiple networks with development. For example, auditory and sensorimotor regions had greater bilateral connectivity with development, whereas regions including the dorsolateral frontal cortex (Brodmann area left 9 and left 46) showed an increase in left lateralization with development. Our findings support a complex, nonlinear association between laterality and age in school-age children, a time when brain function and structure are developing rapidly. We also found brain networks in which laterality was significantly associated with sex, handedness, and intelligence quotient, but we did not find any significant association with behavioral scores. Impact statement Lateralization in brain function has been associated with age and sex in several previous studies; however, there has been less focus on lateralization of functional networks during development. A clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years.
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http://dx.doi.org/10.1089/brain.2020.0863DOI Listing
July 2021

Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.

Brain Connect 2021 Sep 7. Epub 2021 Sep 7.

Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.

Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD. Impact statement By assuming that functional connectivity is static over time, many previous studies have ignored the brain dynamics in Alzheimer's disease (AD) progression. Here, longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in AD progression. The result of this study would increase our understanding of the underlying mechanisms of AD and help in finding future treatment of this neurological disorder.
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http://dx.doi.org/10.1089/brain.2020.0847DOI Listing
September 2021

Sexually dimorphic development in the cortical oscillatory dynamics serving early visual processing.

Dev Cogn Neurosci 2021 08 26;50:100968. Epub 2021 May 26.

Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA. Electronic address:

Successful interaction with one's visual environment is paramount to developing and performing many basic and complex mental functions. Although major aspects of visual development are completed at an early age, other structural and functional components of visual processing appear to be dynamically changing across a much more protracted period extending into late childhood and adolescence. However, the underlying neurophysiological changes and cortical oscillatory dynamics that support maturation of the visual system during this developmental period remain poorly understood. The present study utilized magnetoencephalography (MEG) to investigate maturational changes in the neural dynamics serving basic visual processing during childhood and adolescence (ages 9-15, n = 69). Our key results included robust sex differences in alpha oscillatory activity within the left posterior parietal cortex, and sex-by-age interactions in gamma activity in the right lingual gyrus and superior parietal lobule. Hierarchical regression revealed that the peak frequency of both the alpha and gamma responses predicted response power in parietal regions above and beyond the noted effects of age and sex. These findings affirm the view that neural oscillations supporting visual processing develop over a much more protracted period, and illustrate that these maturational trajectories are influenced by numerous elements, including age, sex, and individual variation.
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http://dx.doi.org/10.1016/j.dcn.2021.100968DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187257PMC
August 2021

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.

Curr Opin Neurol 2021 08;34(4):469-479

Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.

Purpose Of Review: The 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments.

Recent Findings: We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy.

Summary: The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.
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http://dx.doi.org/10.1097/WCO.0000000000000967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263510PMC
August 2021

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.

Brain Connect 2021 Jul 30. Epub 2021 Jul 30.

Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run -means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the -means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.
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http://dx.doi.org/10.1089/brain.2020.0896DOI Listing
July 2021

Microstructural plasticity in nociceptive pathways after spinal cord injury.

J Neurol Neurosurg Psychiatry 2021 May 26. Epub 2021 May 26.

Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, Zurich, Switzerland

Objective: To track the interplay between (micro-) structural changes along the trajectories of nociceptive pathways and its relation to the presence and intensity of neuropathic pain (NP) after spinal cord injury (SCI).

Methods: A quantitative neuroimaging approach employing a multiparametric mapping protocol was used, providing indirect measures of myelination (via contrasts such as magnetisation transfer (MT) saturation, longitudinal relaxation (R1)) and iron content (via effective transverse relaxation rate (R2*)) was used to track microstructural changes within nociceptive pathways. In order to characterise concurrent changes along the entire neuroaxis, a combined brain and spinal cord template embedded in the statistical parametric mapping framework was used. Multivariate source-based morphometry was performed to identify naturally grouped patterns of structural variation between individuals with and without NP after SCI.

Results: In individuals with NP, lower R1 and MT values are evident in the primary motor cortex and dorsolateral prefrontal cortex, while increases in R2* are evident in the cervical cord, periaqueductal grey (PAG), thalamus and anterior cingulate cortex when compared with pain-free individuals. Lower R1 values in the PAG and greater R2* values in the cervical cord are associated with NP intensity.

Conclusions: The degree of microstructural changes across ascending and descending nociceptive pathways is critically implicated in the maintenance of NP. Tracking maladaptive plasticity unravels the intimate relationships between neurodegenerative and compensatory processes in NP states and may facilitate patient monitoring during therapeutic trials related to pain and neuroregeneration.
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http://dx.doi.org/10.1136/jnnp-2020-325580DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292587PMC
May 2021

An Approach to Automatically Label and Order Brain Activity/Component Maps.

Brain Connect 2021 Jul 5. Epub 2021 Jul 5.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, and Emory University, Atlanta, Georgia, USA.

Functional magnetic resonance imaging (fMRI) is a brain imaging technique that provides detailed insights into brain function and its disruption in various brain disorders. The data-driven analysis of fMRI brain activity maps involves several postprocessing steps, the first of which is identifying whether the estimated brain network maps capture signals of interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This is followed by linking the ICNs to standardized anatomical and functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary to facilitate interpretation. Here we develop a novel and efficient method (Autolabeler) for implementing and integrating all of these processes in a fully automated manner. The Autolabeler method is pretrained on a cross-validated elastic-net regularized general linear model from the noisecloud toolbox to separate neuroscientifically meaningful ICNs from artifacts. It is capable of automatically labeling activity maps with labels from several well-known anatomical and functional parcellations. Subsequently, this method also maximizes the modularity within functional domains to generate a more systematically structured FNC matrix for network analyses. Results show that our pretrained model achieves 86% accuracy at classifying ICNs from artifacts in an independent validation data set. The automatic anatomical and functional labels also have a high degree of similarity with manual labels selected by human raters. At a time of ever-increasing rates of generating brain imaging data and analyzing brain activity, the proposed Autolabeler method is intended to automate such analyses for faster and more reproducible research. Impact statement Our proposed method is capable of implementing and integrating some of the crucial tasks in functional magnetic resonance imaging (fMRI) studies. It is the first to incorporate such tasks without the need for expert intervention. We develop an open-source toolbox for the proposed method that can function as stand-alone software and additionally provides seamless integration with the widely used group independent component analysis for fMRI toolbox (GIFT). This integration can aid investigators to conduct fMRI studies in an end-to-end automated manner.
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http://dx.doi.org/10.1089/brain.2020.0950DOI Listing
July 2021

Neural oscillations underlying selective attention follow sexually divergent developmental trajectories during adolescence.

Dev Cogn Neurosci 2021 06 7;49:100961. Epub 2021 May 7.

Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA. Electronic address:

Selective attention processes are critical to everyday functioning and are known to develop through at least young adulthood. Although numerous investigations have studied the maturation of attention systems in the brain, these studies have largely focused on the spatial configuration of these systems; there is a paucity of research on the neural oscillatory dynamics serving selective attention, particularly among youth. Herein, we examined the developmental trajectory of neural oscillatory activity serving selective attention in 53 typically developing youth age 9-to-16 years-old. Participants completed the classic arrow-based flanker task during magnetoencephalography, and the resulting data were imaged in the time-frequency domain. Flanker interference significantly modulated theta and alpha/beta oscillations within prefrontal, mid-cingulate, cuneus, and occipital regions. Interference-related neural activity also increased with age in the temporoparietal junction and the rostral anterior cingulate. Sex-specific effects indicated that females had greater theta interference activity in the anterior insula, whereas males showed differential effects in theta and alpha/beta oscillations across frontoparietal regions. Finally, males showed age-related changes in alpha/beta interference in the cuneus and middle frontal gyrus, which predicted improved behavioral performance. Taken together, these data suggest sexually-divergent developmental trajectories underlying selective attention in youth.
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http://dx.doi.org/10.1016/j.dcn.2021.100961DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131898PMC
June 2021

Resting-state functional connectivity of the human hippocampus in periadolescent children: Associations with age and memory performance.

Hum Brain Mapp 2021 Aug 12;42(11):3620-3642. Epub 2021 May 12.

Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA.

The hippocampus is necessary for declarative (relational) memory, and the ability to form hippocampal-dependent memories develops through late adolescence. This developmental trajectory of hippocampal-dependent memory could reflect maturation of intrinsic functional brain networks, but resting-state functional connectivity (rs-FC) of the human hippocampus is not well-characterized for periadolescent children. Measuring hippocampal rs-FC in periadolescence would thus fill a gap, and testing covariance of hippocampal rs-FC with age and memory could inform theories of cognitive development. Here, we studied hippocampal rs-FC in a cross-sectional sample of healthy children (N = 96; 59 F; age 9-15 years) using a seed-based approach, and linked these data with NIH Toolbox measures, the Picture-Sequence Memory Test (PSMT) and the List Sorting Working Memory Test (LSWMT). The PSMT was expected to rely more on hippocampal-dependent memory than the LSWMT. We observed hippocampal rs-FC with an extensive brain network including temporal, parietal, and frontal regions. This pattern was consistent with prior work measuring hippocampal rs-FC in younger and older samples. We also observed novel, regionally specific variation in hippocampal rs-FC with age and hippocampal-dependent memory but not working memory. Evidence consistent with these findings was observed in a second, validation dataset of similar-age healthy children drawn from the Philadelphia Neurodevelopment Cohort. Further, a cross-dataset analysis suggested generalizable properties of hippocampal rs-FC and covariance with age and memory. Our findings connect prior work by describing hippocampal rs-FC and covariance with age and memory in typically developing periadolescent children, and our observations suggest a developmental trajectory for brain networks that support hippocampal-dependent memory.
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http://dx.doi.org/10.1002/hbm.25458DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249892PMC
August 2021

Ensemble manifold regularized multi-modal graph convolutional network for cognitive ability prediction.

IEEE Trans Biomed Eng 2021 May 11;PP. Epub 2021 May 11.

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks.

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers.

Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches.

Conclusion And Significance: This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.
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http://dx.doi.org/10.1109/TBME.2021.3077875DOI Listing
May 2021

Subtypes of depression characterized by different cognitive decline and brain activity alterations.

J Psychiatr Res 2021 06 29;138:413-419. Epub 2021 Apr 29.

Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China. Electronic address:

Depression is characterized by the heterogeneity in anti-depressant treatment response and clinical outcomes. Cognitive impairment may be one of the more practically important aspects of depression. A new approach was to identify neuropsychologically derived depression subtypes based on the trajectory of neuro-cognition such as intelligence quotient (IQ) change. We used a classical premorbid IQ prediction algorithm and then compared predicted premorbid IQ with current IQ. IQ change was used to delineate the patterns of neuropsychological heterogeneity within a large dataset consisting of 131 patients with major depressive disorder (MDD) and 165 healthy controls (HCs). Neurocognitive results from CANTAB and 3 T resting-state fMRI data were compared among the subgroups identified. IQ change heterogeneity identified two subgroups within the MDD group: preserved IQ (PIQ) and deteriorated IQ (DIQ) in MDD. The DIQ subgroup was marked by poorer functioning across multiple cognition domains, including increased impairments in motor speed, cognitive flexibility, and catastrophic thinking when compared to PIQ and HCs. Moreover, cognitive performance of patients with DIQ was correlated with IQ decline. Also, increased brain activity of anterior cingulate cortex and medial prefrontal cortex was found in DIQ but not in PIQ and HCs. IQ-based subgroups of depression may be differentially associated with the extent of neurocognitive impairment and brain activities, which suggests that classifying the cognitive heterogeneity associated with depression may provide a platform to better characterize the neurobiological underpinnings of the disease.
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http://dx.doi.org/10.1016/j.jpsychires.2021.04.023DOI Listing
June 2021

Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data.

J Neurosci Methods 2021 07 3;358:109214. Epub 2021 May 3.

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA.

Background: Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset.

New Method: We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice.

Comparison: We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia.

Results: The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods.

Conclusion: These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.
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http://dx.doi.org/10.1016/j.jneumeth.2021.109214DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217295PMC
July 2021

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes.

Neuroimage 2021 08 2;237:118129. Epub 2021 May 2.

Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore. Electronic address:

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118129DOI Listing
August 2021

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.

Neuroinformatics 2021 May 4. Epub 2021 May 4.

Georgia State University, Atlanta, GA, USA.

The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially for clinical data or rare disorders). In addition, aggregating data across multiple large studies results in a huge amount of duplicated technical debt and the resources required can be challenging or impossible for an individual site to build. Training on the data distributed across organizations can result in models that generalize much better than models trained on data from any of organizations alone. While there are approaches for decentralized sharing, these often do not provide the highest possible guarantees of sample privacy that only cryptography can provide. In addition, such approaches are often focused on probabilistic solutions. In this paper, we propose an approach that leverages the potential of datasets spread among a number of data collecting organizations by performing joint analyses in a secure and deterministic manner when only encrypted data is shared and manipulated. The approach is based on secure multiparty computation which refers to cryptographic protocols that enable distributed computation of a function over distributed inputs without revealing additional information about the inputs. It enables multiple organizations to train machine learning models on their joint data and apply the trained models to encrypted data without revealing their sensitive data to the other parties. In our proposed approach, organizations (or sites) securely collaborate to build a machine learning model as it would have been trained on the aggregated data of all the organizations combined. Importantly, the approach does not require a trusted party (i.e. aggregator), each contributing site plays an equal role in the process, and no site can learn individual data of any other site. We demonstrate effectiveness of the proposed approach, in a range of empirical evaluations using different machine learning algorithms including logistic regression and convolutional neural network models on human structural and functional magnetic resonance imaging datasets.
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http://dx.doi.org/10.1007/s12021-021-09525-8DOI Listing
May 2021

Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics.

Front Neurosci 2021 13;15:621716. Epub 2021 Apr 13.

The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States.

Background: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.

Methods: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.

Results: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.

Conclusion: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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http://dx.doi.org/10.3389/fnins.2021.621716DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076753PMC
April 2021

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Biol Psychiatry 2021 Jan 30. Epub 2021 Jan 30.

Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.

Background: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.

Methods: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.

Results: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.

Conclusions: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.
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http://dx.doi.org/10.1016/j.biopsych.2021.01.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322149PMC
January 2021
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