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Fredric Schiffer,1,2 Alaptagin Khan,1,2 Kyoko Ohashi,1,2 Laura C Hernandez Garcia,1,2 Carl M Anderson,1,2 Lisa D Nickerson,1,3 Martin H Teicher1,2 1Department of Psychiatry, Harvard Medical School, Boston, MA, USA; 2Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, MA, USA; 3McLean Imaging Center, McLean Hospital, Belmont, MA, USACorrespondence: Fredric Schiffer, Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, MA, USA, Tel +1 617 855 2970, Fax +1 617 855 3712, Email fschiffer@mclean.harvard.eduPurpose: 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.Keywords: cerebral laterality, brain structure, personality, Dual-Brain Psychology, corpus callosum

Abstract: Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. Imaging analyses included age and gynecological age (years since menarche) as covariates. Menstrual pain severity was positively associated with functional connectivity between the cingulo-opercular salience network (cSN) and the sensory processing regions, limbic regions, and insula, and was also positively associated with connectivity between the left central executive network (CEN) and posterior regions. Menstrual pain interference was positively associated with connectivity between the cSN and widespread brain areas. In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain., (Copyright © 2024 International Association for the Study of Pain.)

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., Competing Interests: Dr. Misra has served on the advisory boards of AbbVie and Ipsen, served as a consultant for AbbVie and Sanofi, and served as a Key Opinion Leader for Lumos Pharmaceuticals; she has received study drug donations from Tonix Pharmaceuticals; and she is a contributor to UpToDate. Dr. Goldstein has served on the scientific advisory board of and has an equity interest in Cala Health. Dr. Pizzagalli has served as a consultant for Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sage Therapeutics, Sama Therapeutics, Sunovion Pharmaceuticals, and Takeda Pharmaceuticals; he has received honoraria from the American Psychological Association, the Psychonomic Society, and Springer (for editorial work) and from Alkermes; he has received research funding from the Bird Foundation, the Brain and Behavior Research Foundation, the Dana Foundation, Millennium Pharmaceuticals, and Wellcome Leap; and he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics, and Neuroscience Software. The other authors report no financial relationships with commercial interests.

The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness., (© 2024. The Author(s).)

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

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., (© 2023 Massachusetts Institute of Technology.)

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., (© 2023 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xu, Hao, Zhang, Zhou, Kärkkäinen, Nickerson, Li and Cong.)

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., (© 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Visual components of trauma memories are often vividly re-experienced by survivors with deleterious consequences for normal function. Neuroimaging research on trauma has primarily focused on threat-processing circuitry as core to trauma-related dysfunction. Conversely, limited attention has been given to visual circuitry which may be particularly relevant to posttraumatic stress disorder (PTSD). Prior work suggests that the ventral visual stream is directly related to the cognitive and affective disturbances observed in PTSD and may be predictive of later symptom expression. The present study used multimodal magnetic resonance imaging data (n = 278) collected two weeks after trauma exposure from the AURORA study, a longitudinal, multisite investigation of adverse posttraumatic neuropsychiatric sequelae. Indices of gray and white matter were combined using data fusion to identify a structural covariance network (SCN) of the ventral visual stream 2 weeks after trauma. Participant's loadings on the SCN were positively associated with both intrusion symptoms and intensity of nightmares. Further, SCN loadings moderated connectivity between a previously observed amygdala-hippocampal functional covariance network and the inferior temporal gyrus. Follow-up MRI data at 6 months showed an inverse relationship between SCN loadings and negative alterations in cognition in mood. Further, individuals who showed decreased strength of the SCN between 2 weeks and 6 months had generally higher PTSD symptom severity over time. The present findings highlight a role for structural integrity of the ventral visual stream in the development of PTSD. The ventral visual stream may be particularly important for the consolidation or retrieval of trauma memories and may contribute to efficient reactivation of visual components of the trauma memory, thereby exacerbating PTSD symptoms. Potentially chronic engagement of the network may lead to reduced structural integrity which becomes a risk factor for lasting PTSD symptoms., (© 2022. The Author(s).)

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., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

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., (© 2022 by the Research Society on Alcoholism.)

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., (Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the University of Maryland's school of medicine, Maryland Psychiatric Research Center.)

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., (© 2021. The Author(s).)

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.

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., (© 2022 S. Karger AG, Basel.)

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., (© 2021. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2021 The Authors.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the University of Maryland's school of medicine, Maryland Psychiatric Research Center.)

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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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., (Copyright © 2020 Hu, Waters, Aslan, Frederick, Cong and Nickerson.)

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., (Copyright © 2019. Published by Elsevier Inc.)

Background: The insula has a well-established role in nicotine dependence and is a node of the salience network, which integrates internal and external information to guide behavior. Recent findings reveal that internal and external processing occurs in the ventral anterior insula (vAI) and dorsal anterior insula (dAI), respectively. Whether vAI/dAI network connectivity differentially reflects internally generated craving and externally triggered smoking cue reactivity was tested., Methods: Thirty-six male and female nicotine-dependent individuals smoked 1 hour before functional magnetic resonance imaging. Baseline craving was measured, followed by resting-state and smoking cue reactivity scans and then another assessment of craving. Craving and cue reactivity interactions were measured by focusing on specific nodes of the salience network: the vAI/dAI and anterior cingulate cortex., Results: Resting-state vAI/dAI networks overlapped with the prototypical salience network, yet they possessed distinct patterns, linking the vAI with nodes of the internally focused default mode network and the dAI with nodes of the external, goal-related frontoparietal network. Internally generated baseline craving was associated with enhanced vAI connectivity, whereas rostral anterior cingulate cortex reactivity to external smoking cues was associated with greater dAI connectivity. We also found that cue reactivity in the rostral anterior cingulate cortex was associated with a rise in subjective cue-induced craving, whereas baseline subjective craving did not influence brain cue reactivity., Conclusions: These data show that brain reactivity to smoking cues is associated with a subsequent increase in craving. In addition, separate insula networks have a role in an individual's vulnerability to internally related craving and externally triggered cue reactivity, which could guide the development of new, neurobiologically targeted therapies., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: While many adolescents exhibit risky behavior, teenagers with a family history (FH+) of an alcohol use disorder (AUD) are at a heightened risk for earlier initiation of alcohol use, a more rapid escalation in frequency and quantity of alcohol consumption and developing a subsequent AUD in comparison with youth without such family history (FH-). Neuroanatomically, developmentally normative risk-taking behavior parallels an imbalance between more protracted development of the prefrontal cortex (PFC) and earlier development of limbic regions. Magnetic resonance imaging (MRI)-derived volumetric properties were obtained for these structures in FH+ and FH- adolescents., Methods: Forty-two substance-naïve adolescents (13- to 14-year-olds), stratified into FH+ (N = 19, 13 girls) and FH- (N = 23, 11 girls) age/handedness-matched groups, completed MRI scanning at 3.0T, as well as cognitive and clinical testing. T1 images were processed using FreeSurfer to measure PFC and hippocampi/amygdalae subfields/nuclei volumes., Results: FH+ status was associated with larger hippocampal/amygdala volumes (p < 0.05), relative to FH- adolescents, with right amygdala results appearing to be driven by FH+ boys. Volumetric differences also were positively associated with family history density (p < 0.05) of having an AUD. Larger subfields/nuclei volumes were associated with higher anxiety levels and worse auditory verbal learning performance (p < 0.05)., Conclusions: FH+ risk for AUD is detectable via neuromorphometric characteristics, which precede alcohol use onset and the potential onset of a later AUD, that are associated with emotional and cognitive measures. It is plausible that the development of limbic regions might be altered in FH+ youth, even prior to the onset of alcohol use, which could increase later risk. Thus, targeted preventative measures are warranted that serve to delay the onset of alcohol use in youth, particularly in those who are FH+ for an AUD., (© 2019 by the Research Society on Alcoholism.)

Background: Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions., New Method: The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone., Results: Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables. Comparison with Existing Method: The Kullback-Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20., Conclusion: The current study sheds new light on the importance of temporal course variability in ICA of fMRI data., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Patients with schizophrenia show impairment in processing faces, including facial affect and face detection, but the underlying mechanisms are unknown. We used functional magnetic resonance imaging (fMRI) to characterize resting state functional connectivity between an independent component analysis (ICA)-defined early visual cortical network (corresponding to regions in V1, V2, V3) and a priori defined face-processing regions (fusiform face area [FFA], occipital face area [OFA], superior temporal sulcus [STS] and amygdala) using dual regression in 20 schizophrenia patients and 26 healthy controls. We also investigated the association between resting functional connectivity and neural responses (fMRI) elicited by a face detection paradigm in a partially overlapping sample (Maher et al., 2016) that used stimuli equated for lower-level perceptual abilities. Group differences in functional connectivity were found in right FFA only; controls showed significantly stronger functional connectivity to an early visual cortical network. Functional connectivity in right FFA was associated with (a) neural responses during face detection in controls only, and (b) perceptual detection thresholds for faces in patients only. The finding of impaired functional connectivity for right FFA (but not other queried domain-specific regions) converges with findings investigating face detection in an overlapping sample in which dysfunction was found exclusively for right FFA in schizophrenia during face detection., (Copyright © 2019 Elsevier B.V. All rights reserved.)

There have been many recent reports highlighting a crisis in replication and reliability of research in psychology, neuroscience, and neuroimaging. After a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive neurological, behavioral, and genetics assessments and neuroimaging data from over 1,100 individuals. In the present study, findings supporting the replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks, and novel findings on activation of brain networks during task performance that arose with this exercise are presented as a demonstration of use of the HCP for replication studies.

The frontal cortex undergoes substantial structural and functional changes during adolescence and significant developmental changes also occur in the hippocampus. Both of these regions are notably vulnerable to alcohol and other substance use, which is typically initiated during adolescence. Identifying measures of brain function during adolescence, particularly before initiation of drug or alcohol use, is critical to understanding how such behaviors may affect brain development, especially in these vulnerable brain regions. While there is a substantial developmental literature on adolescent working memory, less is known about spatial memory. Thus, a virtual Morris water task (vMWT) was applied to probe function of the adolescent hippocampus. Multiband blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data were acquired at 3T during task performance. Participants included 32 healthy, alcohol- and drug-naïve adolescents, 13-14 years old, examined at baseline of a 3-year longitudinal MRI study. Significantly greater BOLD activation was observed in the hippocampus and surrounding areas, and in prefrontal regions involved in executive function, during retrieval relative to motor performance. In contrast, significantly greater BOLD activation was observed in components of the default mode network, including frontal medial cortex, during the motor condition (when task demands were minimal) relative to the retrieval condition. Worse performance (longer path length) during retrieval was associated with greater activation of angular gyrus/supramarginal gyrus, whereas worse performance (longer path length/latency) during motor control was associated with less activation of frontal pole. Furthermore, while latency (time to complete task) was greater in females than in males, there were no sex differences in path length (accuracy), suggesting that females required more time to navigate the virtual environment, but did so as effectively as males. These findings demonstrate that performance of the vMWT elicits hippocampal and prefrontal activation patterns in early adolescence, similar to activation observed during spatial memory retrieval in adults. Given that this task is sensitive to hippocampal function, and that the adolescent hippocampus is notably vulnerable to the effects of alcohol and other substances, data acquired using this task during healthy adolescent development may provide a framework for understanding neurobiological impact of later initiation of use.

Focusing on sex differences is necessary to fully understand basic neurobiological processes such as the engagement of large-scale brain networks involved in attention. Prior work suggests that women show enhanced attention during tasks of reward/punishment relative to men. Yet, sex differences in the engagement of neural networks sub serving internal and external focus has been unexplored in regard to reward and punishment. Using data from a large sample (n = 190) of healthy participants from the Human Connectome Project, we investigated sex differences in default mode network (DMN), dorsal attention network (DAN), and frontal parietal network (FPN) activation during exposure to reward and punishment. To determine if sex differences are specific to valenced stimuli, we analyzed network activation during working memory. Results indicate that, relative to men, women have increased suppression of the DMN and greater activation of the DAN during exposure to reward and punishment. Given the relative roles of these networks in internal (DMN) and external (DAN) attention, this pattern of activation suggests that women have enhanced external attention to reward and punishment. In contrast, there were no sex differences in network activation during working memory, indicating that this sex difference is specific to the processing of reward and punishment. These findings suggest a neurobiological explanation for prior work showing women have greater sensitivity to reward/punishment and are more prone to psychiatric disorders characterized by enhanced attention to such stimuli. Furthermore, given the large sample from the Human Connectome Project, the current findings provide general implications for the study of sex as a biological variable in investigation of reward processes., Competing Interests: The authors have declared that no competing interests exist.

Emotional intelligence (EI) is defined as an individual's capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI., (© The Author (2017). Published by Oxford University Press.)

The transition to college is associated with an increase in heavy episodic alcohol use, or binge drinking, during a time when the prefrontal cortex and prefrontal-limbic circuitry continue to mature. Traits associated with this immaturity, including impulsivity in emotional contexts, may contribute to risky and heavy episodic alcohol consumption. The current study used blood oxygen level dependent (BOLD) multiband functional magnetic resonance imaging (fMRI) to assess brain activation during a task that required participants to ignore background images with positive, negative, or neutral emotional valence while performing an inhibitory control task (Go-NoGo). Subjects were 23 college freshmen (seven male, 18-20 years) who engaged in a range of drinking behavior (past 3 months' binge episodes range = 0-19, mean = 4.6, total drinks consumed range = 0-104, mean = 32.0). Brain activation on inhibitory trials (NoGo) was contrasted between negative and neutral conditions and between positive and neutral conditions using non-parametric testing (5000 permutations) and cluster-based thresholding ( z = 2.3), p ≤ 0.05 corrected. Results showed that a higher recent incidence of binge drinking was significantly associated with decreased activation of dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), and anterior cingulate cortex (ACC), brain regions strongly implicated in executive functioning, during negative relative to neutral inhibitory trials. No significant associations between binge drinking and brain activation were observed for positive relative to neutral images. While task performance was not significantly associated with binge drinking in this sample, subjects with heavier recent binge drinking showed decreased recruitment of executive control regions under negative versus neutral distractor conditions. These findings suggest that in young adults with heavier recent binge drinking, processing of negative emotional images interferes more with inhibitory control neurocircuitry than in young adults who do not binge drink often. This pattern of altered frontal lobe activation associated with binge drinking may serve as an early marker of risk for future self-regulation deficits that could lead to problematic alcohol use. These findings underscore the importance of understanding the impact of emotion on cognitive control and associated brain functioning in binge drinking behaviors among young adults.

Background: Most studies of brain white matter (WM) in posttraumatic stress disorder (PTSD) have focused on combat trauma, and often were confounded by neurological and substance dependence comorbidity. This study used tract-based spatial statistics (TBSS) and probabilistic tractography to characterize WM microstructure in a mixed-sex community sample of PTSD patients exposed to diverse and multiple traumas, and in trauma-exposed normal comparison (TENC) subjects., Methods: TBSS compared diffusion measures between 20 adults with DSM-IV PTSD and 17 TENC, using a whole-brain voxel-wise approach. Probabilistic tractography using Freesurfer's TRACULA was employed to measure diffusion tensor imaging (DTI) metrics within anatomically defined pathways. DTI metrics were compared between groups and correlated with PTSD symptom severity and trauma load., Results: Controlling for age, sex, and motion, PTSD subjects had significantly reduced fractional anisotropy (FA) in a left frontal lobe cluster compared with TENC, at p < .05, family-wise error corrected. Tractography identified significant group differences in the inferior longitudinal fasciculus (ILF), including lower FA and higher radial diffusivity in PTSD compared with TENC. Within the PTSD group, FA values were not correlated with symptom severity or trauma load. Results remained significant after removing participants using psychotropic medication or those with comorbid major depression., Conclusions: PTSD patients had reduced WM integrity in left hemisphere frontal WM and temporal-occipital WM tracts, compared to trauma-exposed controls. Reduced frontal FA is consistent with compromised top-down attentional control and emotion regulation in PTSD, while reduced ILF FA may be related to sensory processing and gating abnormalities in this disorder., (© 2017 Wiley Periodicals, Inc.)

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

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or "shape") as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.

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

Face detection, the perceptual capacity to identify a visual stimulus as a face before probing deeper into specific attributes (such as its identity or emotion), is essential for social functioning. Despite the importance of this functional capacity, face detection and its underlying brain mechanisms are not well understood. This study evaluated the roles that the cortical face processing system, which is identified largely through studying other aspects of face perception, play in face detection. Specifically, we used functional magnetic resonance imaging (fMRI) to examine the activations of the fusifom face area (FFA), occipital face area (OFA) and superior temporal sulcus (STS) when face detection was isolated from other aspects of face perception and when face detection was perceptually-equated across individual human participants (n=20). During face detection, FFA and OFA were significantly activated, even for stimuli presented at perceptual-threshold levels, whereas STS was not. During tree detection, however, FFA and OFA were responsive only for highly salient (i.e., high contrast) stimuli. Moreover, activation of FFA during face detection predicted a significant portion of the perceptual performance levels that were determined psychophysically for each participant. This pattern of result indicates that FFA and OFA have a greater sensitivity to face detection signals and selectively support the initial process of face vs. non-face object perception., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Node definition or delineating how the brain is parcellated into individual functionally related regions is the first step to accurately map the human connectome. As a result, parcellation of the human brain has drawn considerable attention in the field of neuroscience. The thalamus is known as a relay in the human brain, with its nuclei sending fibers to the cortical and subcortical regions. Functional magnetic resonance imaging techniques offer a way to parcellate the thalamus in vivo based on its connectivity properties. However, the parcellations from previous studies show that both the number and the distribution of thalamic subdivisions vary with different cortical segmentation methods. In this study, we used an unsupervised clustering method that does not rely on a priori information of the cortical segmentation to parcellate the thalamus. Instead, this approach is based on the intrinsic resting-state functional connectivity profiles of the thalamus with the whole brain. A series of cluster solutions were obtained, and an optimal solution was determined. Furthermore, the validity of our parcellation was investigated through the following: (1) identifying specific resting-state connectivity patterns of thalamic parcels with different brain networks and (2) investigating the task activation and psychophysiological interactions of specific thalamic clusters during 8-Hz flashing checkerboard stimulation with simultaneous finger tapping. Together, the current study provides a reliable parcellation of the thalamus and enhances our understating of thalamic. Furthermore, the current study provides a framework for parcellation that could be potentially extended to other subcortical and cortical regions.

Background: The prevalence of marijuana (MJ) use among youth and its legalization for medical or recreational use has intensified public health endeavors of understanding MJ effects on brain structure and function. Studies indicate that MJ use is related to impaired cognitive performance, and altered functional brain activation and chemistry in adolescents and adults, but MJ effects on brain morphology in emerging adults are less understood., Methods: Fifteen MJ users (age 21.8±3.6, 2 females) and 15 non-user (NU) participants (age 22.3±3.5, 2 females) were included, demographically matched on age, education and alcohol use. High-resolution structural MR images were acquired at 3Tesla. Cortical thickness (CT) and volumetric analyses were performed using Freesurfer. A priori regions of interest (ROI) included orbitofrontal and cingulate cortices, amygdala, hippocampus and thalamus., Results: Whole brain CT analysis did not result in significant group differences in a priori ROIs but revealed MJ users had significantly less CT (i.e., thinness) in right fusiform gyrus (rFG) compared to NU (p<0.05). Thalamic volume was significantly smaller in MJ users compared to NU (right, p=0.05; left, p=0.01) and associated with greater non-planning (p<0.01) and overall impulsivity (p=0.04). There were no other group differences., Conclusions: RFG cortical thinness and smaller thalamic volume in emerging adults is associated with MJ abuse. Furthermore, smaller thalamic volume associated with greater impulsivity contributes to growing evidence that the thalamus is neurobiologically perturbed by MJ use. Collectively, altered thalamic and rFG structural integrity may interfere with their known roles in regulating visuoperceptual and object information processing., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

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

Human brain networks mediating interoceptive, behavioral, and cognitive aspects of glycemic control are not well studied. Using group independent component analysis with dual-regression approach of functional magnetic resonance imaging data, we examined the functional connectivity changes of large-scale resting state networks during sequential euglycemic-hypoglycemic clamp studies in patients with type 1 diabetes and nondiabetic controls and how these changes during hypoglycemia were related to symptoms of hypoglycemia awareness and to concurrent glycosylated hemoglobin (HbA1c) levels. During hypoglycemia, diabetic patients showed increased functional connectivity of the right anterior insula and the prefrontal cortex within the executive control network, which was associated with higher HbA1c. Controls showed decreased functional connectivity of the right anterior insula with the cerebellum/basal ganglia network and of temporal regions within the temporal pole network and increased functional connectivity in the default mode and sensorimotor networks. Functional connectivity reductions in the right basal ganglia were correlated with increases of self-reported hypoglycemic symptoms in controls but not in patients. Resting state networks that showed different group functional connectivity during hypoglycemia may be most sensitive to glycemic environment, and their connectivity patterns may have adapted to repeated glycemic excursions present in type 1 diabetes. Our results suggest that basal ganglia and insula mediation of interoceptive awareness during hypoglycemia is altered in type 1 diabetes. These changes could be neuroplastic adaptations to frequent hypoglycemic experiences. Functional connectivity changes in the insula and prefrontal cognitive networks could also reflect an adaptation to changes in brain metabolic pathways associated with chronic hyperglycemia., Significance Statement: The major factor limiting improved glucose control in type 1 diabetes is the significant increase in hypoglycemia associated with insulin treatment. Repeated exposure to hypoglycemia alters patients' ability to recognize the autonomic and neuroglycopenic symptoms associated with low plasma glucose levels. We examined brain resting state networks during the induction of hypoglycemia in diabetic and control subjects and found differences in networks involved in sensorimotor function, cognition, and interoceptive awareness that were related to chronic levels of glycemic control. These findings identify brain regions that are sensitive to variations in plasma glucose levels and may also provide a basis for understanding the mechanisms underlying the increased incidence of cognitive impairment and affective disorders seen in patients with diabetes., (Copyright © 2015 the authors 0270-6474/15/3511013-12$15.00/0.)

Nicotine dependence is a chronic and difficult to treat disorder. While environmental stimuli associated with smoking precipitate craving and relapse, it is unknown whether smoking cues are cognitively processed differently than neutral stimuli. To evaluate working memory differences between smoking-related and neutral stimuli, we conducted a delay-match-to-sample (DMS) task concurrently with functional magnetic resonance imaging (fMRI) in nicotine-dependent participants. The DMS task evaluates brain activation during the encoding, maintenance and retrieval phases of working memory. Smoking images induced significantly more subjective craving, and greater midline cortical activation during encoding in comparison to neutral stimuli that were similar in content yet lacked a smoking component. The insula, which is involved in maintaining nicotine dependence, was active during the successful retrieval of previously viewed smoking versus neutral images. In contrast, neutral images required more prefrontal cortex-mediated active maintenance during the maintenance period. These findings indicate that distinct brain regions are involved in the different phases of working memory for smoking-related versus neutral images. Importantly, the results implicate the insula in the retrieval of smoking-related stimuli, which is relevant given the insula's emerging role in addiction., (© 2013 Society for the Study of Addiction.)

Background: Individuals with major depressive disorder (MDD) are characterized by maladaptive responses to both positive and negative outcomes, which have been linked to localized abnormal activations in cortical and striatal brain regions. However, the exact neural circuitry implicated in such abnormalities remains largely unexplored., Method: In this study 26 unmedicated adults with MDD and 29 matched healthy controls (HCs) completed a monetary incentive delay task during functional magnetic resonance imaging (fMRI). Psychophysiological interaction (PPI) analyses probed group differences in connectivity separately in response to positive and negative outcomes (i.e. monetary gains and penalties)., Results: Relative to HCs, MDD subjects displayed decreased connectivity between the caudate and dorsal anterior cingulate cortex (dACC) in response to monetary gains, yet increased connectivity between the caudate and a different, more rostral, dACC subregion in response to monetary penalties. Moreover, exploratory analyses of 14 MDD patients who completed a 12-week, double-blind, placebo-controlled clinical trial after the baseline fMRI scans indicated that a more normative pattern of cortico-striatal connectivity pre-treatment was associated with greater improvement in symptoms 12 weeks later., Conclusions: These results identify the caudate as a region with dissociable incentive-dependent dACC connectivity abnormalities in MDD, and provide initial evidence that cortico-striatal circuitry may play a role in MDD treatment response. Given the role of cortico-striatal circuitry in encoding action-outcome contingencies, such dysregulated connectivity may relate to the prominent disruptions in goal-directed behavior that characterize MDD.

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Anhedonia is one of the core symptoms of depression and has been linked to blunted responses to rewarding stimuli in striatal regions. Stress, a key vulnerability factor for depression, has been shown to induce anhedonic behavior, including reduced reward responsiveness in both animals and humans, but the brain processes associated with these effects remain largely unknown in humans. Emerging evidence suggests that stress has dissociable effects on distinct components of reward processing, as it has been found to potentiate motivation/'wanting' during the anticipatory phase but reduce reward responsiveness/'liking' during the consummatory phase. To examine the impact of stress on reward processing, we used a monetary incentive delay (MID) task and an acute stress manipulation (negative performance feedback) in conjunction with functional magnetic resonance imaging (fMRI). Fifteen healthy participants performed the MID task under no-stress and stress conditions. We hypothesized that stress would have dissociable effects on the anticipatory and consummatory phases in reward-related brain regions. Specifically, we expected reduced striatal responsiveness during reward consumption (mirroring patterns previously observed in clinical depression) and increased striatal activation during reward anticipation consistent with non-human findings. Supporting our hypotheses, significant Phase (Anticipation/Consumption)×Stress (Stress/No-stress) interactions emerged in the putamen, nucleus accumbens, caudate and amygdala. Post hoc tests revealed that stress increased striatal and amygdalar activation during anticipation but decreased striatal activation during consumption. Importantly, stress-induced striatal blunting was similar to the profile observed in clinical depression under baseline (no-stress) conditions in prior studies. Given that stress is a pivotal vulnerability factor for depression, these results offer insight to better understand the etiology of this prevalent disorder., (Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.)

Craving is a key aspect of drug dependence that is thought to motivate continued drug use. Numerous brain regions have been associated with craving, suggesting that craving is mediated by a distributed brain network. Whether an increase in subjective craving is associated with enhanced interactions among brain regions was evaluated using resting state functional magnetic imaging (fMRI) in nicotine dependent participants. We focused on craving-related changes in the orbital and medial prefrontal cortex (OMPFC) network, which also included the subgenual anterior cingulate cortex (sgACC) extending into the ventral striatum. Brain regions in the OMPFC network are not only implicated in addiction and reward, but, due to their rich anatomic interconnections, may serve as the site of integration across craving-related brain regions. Subjective craving and resting state fMRI were evaluated twice with an ∼1 hour delay between the scans. Cigarette craving was significantly increased at the end, relative to the beginning of the scan session. Enhanced craving was associated with heightened coupling between the OMPFC network and other cortical, limbic, striatal, and visceromotor brain regions that are both anatomically interconnected with the OMPFC, and have been implicated in addiction and craving. This is the first demonstration confirming that an increase in craving is associated with enhanced brain region interactions, which may play a role in the experience of craving.

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant's fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain., (Copyright © 2013 Elsevier Inc. All rights reserved.)