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.

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.

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.

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.

Deconstructing dissociation: a triple network model of trauma-related dissociation and its subtypes.

Trauma-related pathological dissociation is characterized by disruptions in one's sense of self, perceptual, and affective experience. Dissociation and its trauma-related antecedents disproportionately impact women. However, despite the gender-related prevalence and high individual and societal costs, dissociation remains widely underappreciated in clinical practice. Moreover, dissociation lacks a synthesized neurobiological model across its subtypes. Leveraging the Triple Network Model of psychopathology, we sought to parse heterogeneity in dissociative experience by examining functional connectivity of three core neurocognitive networks as related to: (1) the dimensional dissociation subtypes of depersonalization/derealization and partially-dissociated intrusions; and, (2) the diagnostic category of dissociative identity disorder (DID). Participants were 91 women with and without: a history of childhood trauma, current posttraumatic stress disorder (PTSD), and varied levels of dissociation. Participants provided clinical data about dissociation, PTSD symptoms, childhood maltreatment history, and completed a resting-state functional magnetic resonance imaging scan. We used a novel statistical approach to assess both overlapping and unique contributions of dissociation subtypes. Covarying for age, childhood maltreatment and PTSD severity, we found dissociation was linked to hyperconnectivity within central executive (CEN), default (DN), and salience networks (SN), and decreased connectivity of CEN and SN with other areas. Moreover, we isolated unique connectivity markers associated with depersonalization/derealization in CEN and DN, to partially-dissociated intrusions in CEN, and to DID in CEN. This suggests dissociation subtypes have robust functional connectivity signatures that may serve as targets for PTSD/DID treatment engagement. Our findings underscore dissociation assessment as crucial in clinical care, in particular, to reduce gender-related health disparities.

Individual Differences in Hemispheric Emotional Valence by Computerized Test Correlate with Lateralized Differences in Nucleus Accumbens, Hippocampal and Amygdala Volumes.

Purpose: Conventional theories of hemispheric emotional valence (HEV) postulate fixed hemispheric differences in emotional processing. Schiffer's dual brain psychology proposes that there are prominent individual differences with a substantial subset showing a reversed laterality pattern. He further proposed that hemispheric differences were more akin to differences in personality than in emotional processing. This theory is supported by findings that unilateral treatments, such as transcranial magnetic stimulation, are effective if they accurately target individual differences in laterality. The aim of this paper was to assess if a computer test of hemispheric emotional valence (CTHEV) could effectively identify individual differences in HEV and to ascertain if these individual differences were associated with underlying differences in brain structure and connectivity. Patients and Methods: The CTHEV was administered to 50 (18 male/32 female) right-handed participants, aged 18-19 years, enrolled in a study assessing the neurobiological effects of childhood maltreatment. Based on a literature review, we determined whether CTHEV correlated with lateralized volumes of the nucleus accumbens, amygdala, hippocampus, and subgenual anterior cingulate as well as volume of the corpus callosum. Results: CTHEV scores correlated with laterality indices of the nucleus accumbens (p = 0.00016), amygdala (p = 0.0138) and hippocampus (p = 0.031). A positive left hemispheric valence was associated with a larger left-sided nucleus accumbens and hippocampus and a smaller left amygdala. We identified four eigenvector network centrality DTI measures that predict CTHEV, most notably the left amygdala, and found that CTHEV results correlated with total and segment-specific corpus callosal volumes. Conclusion: Individual differences in HEV can be readily assessed by computer test and correlate with differences in brain structure and connectivity that could provide a mechanistic understanding. These findings provide further support for a revised understanding of HEV and provide a tool that could be used to guide lateralized brain treatments.

Impact of Substance Use Disorder on Between-Network Brain Connectivity in Early Psychosis.

The Triple Network Model of psychopathology identifies the salience network (SN), central executive network (CEN), and default mode network (DMN) as key networks underlying the pathophysiology of psychiatric disorders. In particular, abnormal SN-initiated network switching impacts the engagement and disengagement of the CEN and DMN, and is proposed to lead to the generation of psychosis symptoms. Between-network connectivity has been shown to be abnormal in both substance use disorders (SUD) and psychosis. However, none have studied how SUD affects connectivity between sub-networks of the DMN, SN, and CEN in early stage psychosis (ESP) patients. In this study, we collected data from 113 ESP patients and 50 healthy controls to investigate the effect of SUD on between-network connectivity. In addition, we performed sub-group analysis by exploring whether past SUD vs current SUD co-morbidity, or diagnosis (affective vs non-affective psychosis) had a modulatory effect. Connectivity between four network-pairs, consisting of sub-networks of the SN, CEN, and DMN, was significantly different between ESP patients and controls. Two patterns of connectivity were observed when patients were divided into sub-groups with current vs past SUD. In particular, connectivity between right CEN and the cingulo-opercular salience sub-network (rCEN-CON) showed a gradient effect where the severity of abnormalities increased from no history of SUD to past+ to current+. We also observed diagnosis-specific effects, suggesting non-affective psychosis patients were particularly vulnerable to effects of substance use on rCEN-CON connectivity. Our findings reveal insights into how comorbid SUD affects between-network connectivity and symptom severity in ESP.

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.

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.

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.

Perceived stress and rejection associated with functional network strength during memory retrieval in adolescents.

The brain undergoes substantial structural and functional remodeling during adolescence, including alterations in memory-processing regions influenced by stress. This study evaluated brain activation using functional magnetic resonance imaging (fMRI) during spatial memory performance using a virtual Morris water task (MWT) and examined the associations between default mode network (DMN) activation, task performance, and perceived stress and rejection. Functional magnetic resonance imaging data were acquired at 3 Tesla from 59 (34 female) adolescents (13-14 years). The NIH Emotion Toolbox was used to measure perceived stress and rejection. During the MWT, hippocampus and prefrontal cortex showed greater activation during memory retrieval relative to motor performance. Templates of brain functional networks from the Human Connectome Project study were used to extract individual participants' brain network activation strengths for the retrieval > motor contrast for two sub-networks of the default mode network: medial temporal lobe (MTL-DMN) and dorsomedial prefrontal (dMPFC-DMN). For the MTL-DMN sub-network only, activation was significantly associated with worse MWT performance (p = .008) and greater perceived stress (p = .008) and perceived rejection (p = .002). Further, MWT performance was negatively associated with perceived rejection (p = .007). These findings suggest that perceived stress and rejection are related to engagement of MTL-DMN during spatial memory and that engagement of this network impacts performance. These findings also demonstrate the utility of examining task-related network activation strength to identify the impact of perceived stress and rejection on large-scale brain network functioning during adolescence.

Sex-specific neural responses to acute psychosocial stress in depression.

Major Depressive Disorder (MDD) is characterized by increased stress sensitivity. Emerging findings in healthy adults suggest that stress responses within limbic/striatal-prefrontal regions are moderated by sex and unfold over time. Thus, we hypothesized that stress response abnormalities in MDD might be affected by sex and stress exposure time. The Montreal Imaging Stress Task was administered to 124 unmedicated patients with first-episode MDD (76 females) and 243 healthy controls (HC; 137 females) during functional magnetic resonance imaging (fMRI). Based on prior studies, amygdala, hippocampus, medial orbitofrontal cortex (mOFC), nucleus accumbens (NAc) and dorsolateral prefrontal cortex (dlPFC) were selected as a priori regions of interest. In a complementary approach, we probed the effects of stress on the frontoparietal network (FPN) and a network including the amygdala, NAc and anterior cingulate cortex (ACC). Across groups, males exhibited higher dlPFC activity and right FPN amplitude than females. Relative to female HCs, the female MDD group had less deactivation in limbic/striatal regions (amygdala, NAc, hippocampus, Amygdala-NAc-ACC network). Furthermore, unlike female HCs, the female MDD group failed to show a significant increase of deactivation over stress exposure time in the amygdala, mOFC and NAc. Our findings confirm the importance of considering sex differences when investigating neural stress responses. Case-control differences in neural stress responses observed in females (but not males) provide insights into sex differences in the etiology and pathophysiology of depression. The failure to deactivate limbic/NAc regions in depressed females point to dysfunction of adaptive stress responses over stress exposure time.

Acute Posttraumatic Symptoms Are Associated With Multimodal Neuroimaging Structural Covariance Patterns: A Possible Role for the Neural Substrates of Visual Processing in Posttraumatic Stress Disorder.

BACKGROUND: Although aspects of brain morphology have been associated with chronic posttraumatic stress disorder (PTSD), limited work has investigated multimodal patterns in brain morphology that are linked to acute posttraumatic stress severity. In the present study, we utilized multimodal magnetic resonance imaging to investigate if structural covariance networks (SCNs) assessed acutely following trauma were linked to acute posttraumatic stress severity. METHODS: Structural magnetic resonance imaging data were collected around 1 month after civilian trauma exposure in 78 participants. Multimodal magnetic resonance imaging data fusion was completed to identify combinations of SCNs, termed structural covariance profiles (SCPs), related to acute posttraumatic stress severity collected at 1 month. Analyses assessed the relationship between participant SCP loadings, acute posttraumatic stress severity, the change in posttraumatic stress severity from 1 to 12 months, and depressive symptoms. RESULTS: We identified an SCP that reflected greater gray matter properties of the anterior temporal lobe, fusiform face area, and visual cortex (i.e., the ventral visual stream) that varied curvilinearly with acute posttraumatic stress severity and the change in PTSD symptom severity from 1 to 12 months. The SCP was not associated with depressive symptoms. CONCLUSIONS: We identified combinations of multimodal SCNs that are related to variability in PTSD symptoms in the early aftermath of trauma. The identified SCNs may reflect patterns of neuroanatomical organization that provide unique insight into acute posttraumatic stress. Furthermore, these multimodal SCNs may be potential candidates for neural markers of susceptibility to both acute posttraumatic stress and the future development of PTSD.

Reductions in rostral anterior cingulate GABA are associated with stress circuitry in females with major depression: a multimodal imaging investigation.

The interplay between cortical and limbic regions in stress circuitry calls for a neural systems approach to investigations of acute stress responses in major depressive disorder (MDD). Advances in multimodal imaging allow inferences between regional neurotransmitter function and activation in circuits linked to MDD, which could inform treatment development. The current study investigated the role of the inhibitory neurotransmitter GABA in stress circuitry in females with current and remitted MDD. Multimodal imaging data were analyzed from 49 young female adults across three groups (current MDD, remitted MDD (rMDD), and healthy controls). GABA was assessed at baseline using magnetic resonance spectroscopy, and functional MRI data were collected before, during, and after an acute stressor and analyzed using a network modeling approach. The MDD group showed an overall lower cortisol response than the rMDD group and lower rostral anterior cingulate cortex (ACC) GABA than healthy controls. Across groups, stress decreased activation in the frontoparietal network (FPN) but increased activation in the default mode network (DMN) and a network encompassing the ventromedial prefrontal cortex-striatum-anterior cingulate cortex (vmPFC-Str-ACC). Relative to controls, the MDD and rMDD groups were characterized by decreased FPN and salience network (SN) activation overall. Rostral ACC GABA was positively associated with connectivity between an overlapping limbic network (Temporal-Insula-Amygdala) and two other circuits (FPN and DMN). Collectively, these findings indicate that reduced GABA in females with MDD was associated with connectivity differences within and across key networks implicated in depression. GABAergic treatments for MDD might alleviate stress circuitry abnormalities in females.

Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

BACKGROUND: Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored. NEW METHOD: In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework. RESULT: The difference between PD and HC mainly exists at frequency band 0.04-0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale. COMPARISON WITH EXISTING METHODS: Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients. CONCLUSION: SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered.

Abnormal Brain Bioenergetics in First-Episode Psychosis.

BACKGROUND: Converging evidence indicates impaired brain energy metabolism in schizophrenia and other psychotic disorders. Creatine kinase (CK) is pivotal in providing adenosine triphosphate in the cell and maintaining its levels when energy demand is increased. However, the activity of CK has not been investigated in patients with first-episode schizophrenia spectrum disorders. METHODS: Using in vivo phosphorus magnetization transfer spectroscopy, we measured CK first-order forward rate constant (k f ) in the frontal lobe, in patients with first-episode psychosis (FEP; n = 16) and healthy controls (n = 34), at rest. RESULTS: CK k f was significantly reduced in FEP compared to healthy controls. There were no differences in other energy metabolism-related measures, including phosphocreatine (PCr) or ATP, between groups. We also found increase in glycerol-3-phosphorylcholine, a putative membrane breakdown product, in patients. CONCLUSIONS: The results of this study indicate that brain bioenergetic abnormalities are already present early in the course of schizophrenia spectrum disorders. Future research is needed to identify the relationship of reduced CK k f with psychotic symptoms and to test treatment alternatives targeting this pathway. Increased glycerol-3-phosphorylcholine is consistent with earlier studies in medication-naïve patients and later studies in first-episode schizophrenia, and suggest enhanced synaptic pruning.

Brain Activation during Memory Retrieval is Associated with Depression Severity in Women.

Major depressive disorder (MDD) is a debilitating disorder that interferes with daily functioning, and that occurs at higher rates in women than in men. Structural and functional alterations in hippocampus and frontal lobe have been reported in MDD, which likely contribute to the multifaceted nature of MDD. One area impacted by MDD is hippocampal-mediated memory, which can be probed using a spatial virtual Morris water task (MWT). Women (n=24) across a spectrum of depression severity underwent functional magnetic resonance imaging (fMRI) during MWT. Depression severity, assessed via Beck Depression Inventory (BDI), was examined relative to brain activation during task performance. Significant brain activation was evident in areas traditionally implicated in spatial memory processing, including right hippocampus and frontal lobe regions, for retrieval > motor contrast. When BDI was included as a regressor, significantly less functional activation was evident in left hippocampus, and other non-frontal, task relevant regions for retrieval > rest contrast. Consistent with previous studies, depression severity was associated with functional alterations observed during spatial memory performance. These findings may contribute to understanding neurobiological underpinnings of depression severity and associated memory impairments, which may have implications for treatment approaches aimed at alleviating effects of depression in women.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables.

Background: Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. Methods: This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. Results: Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. Conclusions: Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA.

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.