Publications by authors named "Bryon Mueller"

131 Publications

Social behaviors and gray matter volumes of brain areas supporting social cognition in children and adolescents with prenatal alcohol exposure.

Brain Res 2021 Feb 20:147388. Epub 2021 Feb 20.

University of Minnesota, Twin Cities, Minneapolis, MN, United States. Electronic address:

The goal of this study was to examine: 1) differences in parent-reported prosocial and antisocial behaviors between children and adolescents with and without prenatal alcohol exposure (PAE); 2) differences in gray matter volumes of brain areas supporting social cognition between children and adolescents with and without PAE; 3) correlations between gray matter volumes of brain areas supporting social cognition and parent-reported prosocial and antisocial behaviors. Parents of children and adolescents ages 8-16 years completed measures on their prosocial and antisocial behaviors (i.e., Behavior Assessment Scale for Children, Vineland Adaptive Behaviors Scales, and Child Behavior Checklist) (n = 84; 41 with PAE, 43 without PAE). Seventy-nine participants (40 with PAE, 39 without PAE) also completed a structural Magnetic Resonance Imaging (MRI) scan with quality data. Gray matter volumes of seven brain areas supporting social cognitive processes were computed using automated procedures (FreeSurfer 6.0): bilateral fusiform gyrus, superior temporal gyrus, medial orbitofrontal cortex, lateral orbitofrontal cortex, posterior cingulate cortex, precuneus, and temporal pole. Children and adolescents with PAE showed decreased prosocial behaviors and increased antisocial behaviors as well as smaller volumes of the precuneus and lateral orbitofrontal cortex, even when controlling for total intracranial volume. Social brain volumes were not significantly correlated with prosocial or antisocial behaviors. These findings suggest that children and adolescents with PAE show worse social functioning and smaller volumes of brain areas supporting self-awareness, perspective-taking and emotion-regulation than their same-age peers without PAE.
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http://dx.doi.org/10.1016/j.brainres.2021.147388DOI Listing
February 2021

Accelerated maturation in functional connectivity following early life stress: Circuit specific or broadly distributed?

Dev Cogn Neurosci 2021 Jan 20;48:100922. Epub 2021 Jan 20.

Institute of Child Development, University of Minnesota, 51 East River Road, Minneapolis, MN, 55455, USA.

Psychosocial acceleration theory and other frameworks adapted from life history predict a link between early life stress and accelerated maturation in several physiological systems. Those findings led researchers to suggest that the emotion-regulatory brain circuits of previously-institutionalized (PI) youth are more mature than youth raised in their biological families (non-adopted, or NA, youth) during emotion tasks. Whether this accelerated maturation is evident during resting-state fMRI has not yet been established. Resting-state fMRI data from 83 early adolescents (M = 12.9 years, SD = 0.57 years) including 41 PI and 42 NA youth, were used to examine seed-based functional connectivity between the amygdala and ventromedial prefrontal cortex (vmPFC). Additional whole-brain analyses assessed group differences in functional connectivity and associations with cognitive performance and behavior. We found group differences in amygdala - vmPFC connectivity that may be consistent with accelerated maturation following early life stress. Further, whole-brain connectivity analyses revealed group differences associated with internalizing and externalizing symptoms. However, the majority of whole-brain results were not consistent with an accelerated maturation framework. Our results suggest early life stress in the form of institutional care is associated with circuit-specific alterations to a frontolimbic emotion-regulatory system, while revealing limited differences in more broadly distributed networks.
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http://dx.doi.org/10.1016/j.dcn.2021.100922DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848776PMC
January 2021

Coordination between frontolimbic resting state connectivity and hypothalamic-pituitary-adrenal axis functioning in adolescents with and without depression.

Psychoneuroendocrinology 2021 Mar 28;125:105123. Epub 2020 Dec 28.

Psychology Department, College of Liberal Arts, University of Minnesota, Twin Cities, United States. Electronic address:

Depression is associated with abnormalities in Hypothalamic-Pituitary-Adrenal (HPA) axis functioning and neural circuitry that underlie the stress response. Resting-state functional connectivity (RSFC) between frontolimbic brain regions captures intrinsic connections that may set the stage for the rallying and regulating of the HPA axis system. This study examined the association between cortisol stress response and frontolimbic (amygdala and ventral and dorsal medial prefrontal cortex [vmPFC and dmPFC respectively]) RSFC in 88 (Age: M = 15.95, SD = 2.04; 71.60% female) adolescents with (N = 55) and without (N = 33) major depressive disorder (MDD). We collected salivary cortisol in the context of a modified Trier Social Stress Test (TSST) paradigm. Key findings were that adolescents with depression and healthy controls showed different patterns of association between amygdala and vmPFC RSFC and HPA functioning: while healthy controls showed a positive relationship between frontolimbic connectivity and cortisol levels that may indicate coordination across neural and neuroendocrine systems, adolescents with depression showed a minimal or inverse relationship, suggesting poor coordination of these systems. Results were similar when examining non-suicidal self-injury subgroups within the MDD sample. These findings suggest that the intrinsic quality of this frontolimbic connection may be related to HPA axis functioning. In MDD, inverse associations may represent a compensatory response in one system in response to dysfunction in the other. Longitudinal multilevel research, however, is needed to disentangle how stress system coordination develops in normal and pathological contexts and how these systems recover with treatment.
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http://dx.doi.org/10.1016/j.psyneuen.2020.105123DOI Listing
March 2021

Resting State Hypoconnectivity of Reward Networks in Binge Eating Disorder.

Cereb Cortex 2021 Jan 8. Epub 2021 Jan 8.

Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, 55454 MN, USA.

The clinical presentation of binge eating disorder (BED) and data emerging from task-based functional neuroimaging research suggests that this disorder may be associated with alterations in reward processing. However, there is a dearth of research investigating the functional organization of brain networks that mediate reward in BED. To address this gap, 27 adults with BED and 21 weight-matched healthy controls (WMC) completed a multimodel assessment consisting of a resting functional magnetic resonance imaging scan, behavioral tasks measuring reward-based decision-making (i.e., delay discounting and reversal learning), and self-report assessing clinical symptoms. A seed-based approach was employed to examine the resting state functional connectivity (rsFC) of the striatum (nucleus accumbens [NAcc] and ventral and dorsal caudate), a collection of regions implicated in reward processing. Compared with WMC, the BED group exhibited lower rsFC of striatal seeds, with frontal regions mediating executive functioning (e.g., superior frontal gyrus [SFG]) and posterior, parietal, and temporal regions implicated in emotional processing. Lower NAcc-SFG rsFC was associated with more difficulties with reversal learning and binge eating frequency in the BED group. Results suggest that hypoconnectivity of striatal networks that integrate self-regulation and reward processing may promote the clinical phenomenology of BED. Interventions for BED may benefit from targeting these circuit-based disturbances.
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http://dx.doi.org/10.1093/cercor/bhaa369DOI Listing
January 2021

Hippocampal subfield abnormalities and memory functioning in children with fetal alcohol Spectrum disorders.

Neurotoxicol Teratol 2021 Jan-Feb;83:106944. Epub 2020 Nov 21.

University of Minnesota, Twin Cities, United States of America. Electronic address:

Background: Prenatal alcohol exposure (PAE) affects early brain development and has been associated with hippocampal damage. Animal models of PAE have suggested that some subfields of the hippocampus may be more susceptible to damage than others. Recent advances in structural MRI processing now allow us to examine the morphology of hippocampal subfields in humans with PAE.

Method: Structural MRI scans were collected from 40 children with PAE and 39 typically developing children (ages 8-16). The images were processed using the Human Connectome Project Minimal Preprocessing Pipeline (v4.0.1) and the Hippocampal Subfields package (v21) from FreeSurfer. Using a large dataset of typically developing children enrolled in the Human Connectome Project in Development (HCP-D) for normative standards, we computed age-specific volumetric z-scores for our two samples. Using these norm-adjusted hippocampal subfield volumes, comparisons were performed between children with PAE and typically developing children, controlling for total intracranial volume. Lastly, we investigated whether subfield volumes correlated with episodic memory (i.e., Picture Sequence Memory test of the NIH toolbox).

Results: Five subfields had significantly smaller adjusted volumes in children with PAE than in typically developing controls: CA1, CA4, subiculum, presubiculum, and the hippocampal tail. Subfield volumes were not significantly correlated with episodic memory.

Conclusions: The results suggest that several regions of the hippocampus may be particularly affected by PAE. The finding of smaller CA1 volumes parallels previous reports in rodent models. The novel findings of decreased volume in the subicular cortex, CA4 and the hippocampal tail suggest avenues for future research.
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http://dx.doi.org/10.1016/j.ntt.2020.106944DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855420PMC
November 2020

Cortical thickness and resting-state cardiac function across the lifespan: A cross-sectional pooled mega-analysis.

Psychophysiology 2020 Oct 10. Epub 2020 Oct 10.

Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Understanding the association between autonomic nervous system [ANS] function and brain morphology across the lifespan provides important insights into neurovisceral mechanisms underlying health and disease. Resting-state ANS activity, indexed by measures of heart rate [HR] and its variability [HRV] has been associated with brain morphology, particularly cortical thickness [CT]. While findings have been mixed regarding the anatomical distribution and direction of the associations, these inconsistencies may be due to sex and age differences in HR/HRV and CT. Previous studies have been limited by small sample sizes, which impede the assessment of sex differences and aging effects on the association between ANS function and CT. To overcome these limitations, 20 groups worldwide contributed data collected under similar protocols of CT assessment and HR/HRV recording to be pooled in a mega-analysis (N = 1,218 (50.5% female), mean age 36.7 years (range: 12-87)). Findings suggest a decline in HRV as well as CT with increasing age. CT, particularly in the orbitofrontal cortex, explained additional variance in HRV, beyond the effects of aging. This pattern of results may suggest that the decline in HRV with increasing age is related to a decline in orbitofrontal CT. These effects were independent of sex and specific to HRV; with no significant association between CT and HR. Greater CT across the adult lifespan may be vital for the maintenance of healthy cardiac regulation via the ANS-or greater cardiac vagal activity as indirectly reflected in HRV may slow brain atrophy. Findings reveal an important association between CT and cardiac parasympathetic activity with implications for healthy aging and longevity that should be studied further in longitudinal research.
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http://dx.doi.org/10.1111/psyp.13688DOI Listing
October 2020

Diagnosis of Myotonic Dystrophy Based on Resting State fMRI Using Convolutional Neural Networks.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1714-1717

Myotonic dystrophies (DM) are neuromuscular conditions that cause widespread effects throughout the body. There are brain white matter changes on MRI in patients with DM that correlate with neuropsychological functional changes. How these brain alterations causally relate to the presence and severity of cognitive symptoms remains largely unknown. Deep neural networks have significantly improved the performance of image classification of huge datasets. However, its application in brain imaging is limited and not well described, due to the scarcity of labeled training data. In this work, we propose an approach for the diagnosis of DM based on a spatio-temporal deep learning paradigm. The obtained accuracy (73.71%) and sensitivities and specificities showed that the implemented approach based on 4-D convolutional neural networks leads to a compact, discriminative, and fast computing DM-based clinical medical decision support system.Clinical relevance- Many adults with DM experience cognitive and neurological effects impacting their quality of life, and ability to maintain employment. A robust and reliable DM-based clinical decision support system may help reduce the long diagnostic delay common to DM. Furthermore, it can help neurologists better understand the pathophysiology of the disease and analyze effects of new drugs that aim to address the neurological symptoms of DM.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176455DOI Listing
July 2020

Neural and Behavioral Correlates of Clinical Improvement to Ketamine in Adolescents With Treatment Resistant Depression.

Front Psychiatry 2020 18;11:820. Epub 2020 Aug 18.

Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Minnesota, Twin Cities, MN, United States.

Treatment-resistant depression (TRD) is a serious problem in adolescents. Development and optimization of novel interventions for these youth will require a deeper knowledge of the neurobiology of depression. A well-established phenomenon of depression is an attention bias toward negativity and away from positivity that is evidenced behaviorally and neurally, but it is unclear how symptom reduction is related to changes to this bias. Neurobiological research using a treatment probe has promise to help discover the neural changes that accompany symptom improvement. Ketamine has utility for such research because of its known rapid and strong antidepressant effects in the context of TRD. Our previous study of six open-label ketamine infusions in 11 adolescents with TRD showed variable response, ranging from full remission, partial response, non-response, or clinical worsening. In this study, we examined the performance of these participants on Word Face Stroop (WFS) fMRI task where they indicated the valence of affective words superimposed onto either congruent or incongruent emotional faces before and after the ketamine infusions. Participants also completed a clinical assessment (including measurement of depression symptomology and anhedonia/pleasure) before and after the ketamine infusions. Following ketamine treatment, better WFS performance correlated with self-reported decreased depressive symptoms and increased pleasure. Analyses of corticolimbic, corticostriatal and default mode (DMN) networks showed that across networks, decreased activation during all conditions (congruent negative, congruent positive, incongruent negative, and incongruent positive) correlated with decreases in depressive symptoms and with increases in pleasure. These findings suggest that in adolescents with TRD, clinical improvement may require an attenuation of the negativity bias and re-tuning of these three critical neural networks to attenuate DMN and limbic regions activation and allow more efficient recruitment of the reward network. Lower activation across conditions may facilitate shifting across different salient emotional stimuli rather than getting trapped in downward negative spirals.
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http://dx.doi.org/10.3389/fpsyt.2020.00820DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461781PMC
August 2020

Brain entropy and neurotrophic molecular markers accompanying clinical improvement after ketamine: Preliminary evidence in adolescents with treatment-resistant depression.

J Psychopharmacol 2021 Feb 9;35(2):168-177. Epub 2020 Jul 9.

Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota, Minneapolis, USA.

Background: Current theory suggests that treatment-resistant depression (TRD) involves impaired neuroplasticity resulting in cognitive and neural rigidity, and that clinical improvement may require increasing brain flexibility and adaptability.

Aims: In this hypothesis-generating study, we sought to identify preliminary evidence of brain flexibility correlates of clinical change within the context of an open-label ketamine trial in adolescents with TRD, focusing on two promising candidate markers of neural flexibility: (a) entropy of resting-state functional magnetic resonance imaging (fMRI) signals; and (b) insulin-stimulated phosphorylation of mammalian target of rapamycin (mTOR) and glycogen synthase-3-beta (GSK3β) in peripheral blood mononuclear cells.

Methods: We collected resting-state functional magnetic resonance imaging data and blood samples from 13 adolescents with TRD before and after a series of six ketamine infusions over 2 weeks. Usable pre/post ketamine data were available from 11 adolescents for imaging and from 10 adolescents for molecular signaling. We examined correlations between treatment response and changes in the central and peripheral flexibility markers.

Results: Depression reduction correlated with increased nucleus accumbens entropy. Follow-up analyses suggested that physiological changes were associated with treatment response. In contrast to treatment non-responders (=6), responders (=5) showed greater increase in nucleus accumbens entropy after ketamine, together with greater post-treatment insulin/mTOR/GSK3β signaling.

Conclusions: These data provide preliminary evidence that changes in neural flexibility may underlie symptom relief in adolescents with TRD following ketamine. Future research with adequately powered samples is needed to confirm resting-state entropy and insulin-stimulated mTOR and GSK3β as brain flexibility markers and candidate targets for future clinical trials.

Clinical Trial Name: Ketamine in adolescents with treatment-resistant depression https://clinicaltrials.gov/ct2/show/NCT02078817 NCT02078817.
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http://dx.doi.org/10.1177/0269881120928203DOI Listing
February 2021

Para-limbic Structural Abnormalities Are Associated With Internalizing Symptoms in Children With Prenatal Alcohol Exposure.

Alcohol Clin Exp Res 2020 08 1;44(8):1598-1608. Epub 2020 Jul 1.

University of Minnesota, Twin Cities, Minneapolis, Minnesota.

Background: Prenatal alcohol exposure (PAE) is associated with a variety of structural abnormalities in the brain, including several within the para-limbic system. Children with PAE have higher rates of internalizing disorders, including depression and anxiety, which may be related to underlying limbic system anomalies.

Methods: Children aged 8 to 16 with PAE (n = 41) or without PAE (n = 36) underwent an magnetic resonance imaging of the brain and parents completed behavioral questionnaires about their children. Semi-automated procedures (FreeSurfer) were used to derive para-limbic volumes from T1-weighted anatomical images.

Results: There were significant group differences (PAE vs. nonexposed controls) in the caudate, hippocampus, and the putamen; children with PAE had smaller volumes in these regions even after controlling for total intracranial volume. A trend-level association was seen between caudate volume and internalizing symptoms in children with PAE; smaller caudate volumes (presumably reflecting less optimal neurodevelopment) were associated with higher levels of anxiety and depression symptoms in these children.

Conclusions: Caudate structure may be disproportionately affected by PAE and may be associated with the later development of internalizing symptoms in those affected by PAE.
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http://dx.doi.org/10.1111/acer.14390DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484415PMC
August 2020

Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

Mol Psychiatry 2020 May 28. Epub 2020 May 28.

Department of Psychiatry, University of Münster, Münster, Germany.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.
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http://dx.doi.org/10.1038/s41380-020-0774-9DOI Listing
May 2020

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Authors:
Laura K M Han Richard Dinga Tim Hahn Christopher R K Ching Lisa T Eyler Lyubomir Aftanas Moji Aghajani André Aleman Bernhard T Baune Klaus Berger Ivan Brak Geraldo Busatto Filho Angela Carballedo Colm G Connolly Baptiste Couvy-Duchesne Kathryn R Cullen Udo Dannlowski Christopher G Davey Danai Dima Fabio L S Duran Verena Enneking Elena Filimonova Stefan Frenzel Thomas Frodl Cynthia H Y Fu Beata R Godlewska Ian H Gotlib Hans J Grabe Nynke A Groenewold Dominik Grotegerd Oliver Gruber Geoffrey B Hall Ben J Harrison Sean N Hatton Marco Hermesdorf Ian B Hickie Tiffany C Ho Norbert Hosten Andreas Jansen Claas Kähler Tilo Kircher Bonnie Klimes-Dougan Bernd Krämer Axel Krug Jim Lagopoulos Ramona Leenings Frank P MacMaster Glenda MacQueen Andrew McIntosh Quinn McLellan Katie L McMahon Sarah E Medland Bryon A Mueller Benson Mwangi Evgeny Osipov Maria J Portella Elena Pozzi Liesbeth Reneman Jonathan Repple Pedro G P Rosa Matthew D Sacchet Philipp G Sämann Knut Schnell Anouk Schrantee Egle Simulionyte Jair C Soares Jens Sommer Dan J Stein Olaf Steinsträter Lachlan T Strike Sophia I Thomopoulos Marie-José van Tol Ilya M Veer Robert R J M Vermeiren Henrik Walter Nic J A van der Wee Steven J A van der Werff Heather Whalley Nils R Winter Katharina Wittfeld Margaret J Wright Mon-Ju Wu Henry Völzke Tony T Yang Vasileios Zannias Greig I de Zubicaray Giovana B Zunta-Soares Christoph Abé Martin Alda Ole A Andreassen Erlend Bøen Caterina M Bonnin Erick J Canales-Rodriguez Dara Cannon Xavier Caseras Tiffany M Chaim-Avancini Torbjørn Elvsåshagen Pauline Favre Sonya F Foley Janice M Fullerton Jose M Goikolea Bartholomeus C M Haarman Tomas Hajek Chantal Henry Josselin Houenou Fleur M Howells Martin Ingvar Rayus Kuplicki Beny Lafer Mikael Landén Rodrigo Machado-Vieira Ulrik F Malt Colm McDonald Philip B Mitchell Leila Nabulsi Maria Concepcion Garcia Otaduy Bronwyn J Overs Mircea Polosan Edith Pomarol-Clotet Joaquim Radua Maria M Rive Gloria Roberts Henricus G Ruhe Raymond Salvador Salvador Sarró Theodore D Satterthwaite Jonathan Savitz Aart H Schene Peter R Schofield Mauricio H Serpa Kang Sim Marcio Gerhardt Soeiro-de-Souza Ashley N Sutherland Henk S Temmingh Garrett M Timmons Anne Uhlmann Eduard Vieta Daniel H Wolf Marcus V Zanetti Neda Jahanshad Paul M Thompson Dick J Veltman Brenda W J H Penninx Andre F Marquand James H Cole Lianne Schmaal

Mol Psychiatry 2020 May 18. Epub 2020 May 18.

Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
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http://dx.doi.org/10.1038/s41380-020-0754-0DOI Listing
May 2020

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

NMR Biomed 2020 06 23;33(6):e4294. Epub 2020 Mar 23.

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

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.
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http://dx.doi.org/10.1002/nbm.4294DOI Listing
June 2020

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

NMR Biomed 2020 06 23;33(6):e4294. Epub 2020 Mar 23.

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

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.
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http://dx.doi.org/10.1002/nbm.4294DOI Listing
June 2020

The genetic architecture of the human cerebral cortex.

Authors:
Katrina L Grasby Neda Jahanshad Jodie N Painter Lucía Colodro-Conde Janita Bralten Derrek P Hibar Penelope A Lind Fabrizio Pizzagalli Christopher R K Ching Mary Agnes B McMahon Natalia Shatokhina Leo C P Zsembik Sophia I Thomopoulos Alyssa H Zhu Lachlan T Strike Ingrid Agartz Saud Alhusaini Marcio A A Almeida Dag Alnæs Inge K Amlien Micael Andersson Tyler Ard Nicola J Armstrong Allison Ashley-Koch Joshua R Atkins Manon Bernard Rachel M Brouwer Elizabeth E L Buimer Robin Bülow Christian Bürger Dara M Cannon Mallar Chakravarty Qiang Chen Joshua W Cheung Baptiste Couvy-Duchesne Anders M Dale Shareefa Dalvie Tânia K de Araujo Greig I de Zubicaray Sonja M C de Zwarte Anouk den Braber Nhat Trung Doan Katharina Dohm Stefan Ehrlich Hannah-Ruth Engelbrecht Susanne Erk Chun Chieh Fan Iryna O Fedko Sonya F Foley Judith M Ford Masaki Fukunaga Melanie E Garrett Tian Ge Sudheer Giddaluru Aaron L Goldman Melissa J Green Nynke A Groenewold Dominik Grotegerd Tiril P Gurholt Boris A Gutman Narelle K Hansell Mathew A Harris Marc B Harrison Courtney C Haswell Michael Hauser Stefan Herms Dirk J Heslenfeld New Fei Ho David Hoehn Per Hoffmann Laurena Holleran Martine Hoogman Jouke-Jan Hottenga Masashi Ikeda Deborah Janowitz Iris E Jansen Tianye Jia Christiane Jockwitz Ryota Kanai Sherif Karama Dalia Kasperaviciute Tobias Kaufmann Sinead Kelly Masataka Kikuchi Marieke Klein Michael Knapp Annchen R Knodt Bernd Krämer Max Lam Thomas M Lancaster Phil H Lee Tristram A Lett Lindsay B Lewis Iscia Lopes-Cendes Michelle Luciano Fabio Macciardi Andre F Marquand Samuel R Mathias Tracy R Melzer Yuri Milaneschi Nazanin Mirza-Schreiber Jose C V Moreira Thomas W Mühleisen Bertram Müller-Myhsok Pablo Najt Soichiro Nakahara Kwangsik Nho Loes M Olde Loohuis Dimitri Papadopoulos Orfanos John F Pearson Toni L Pitcher Benno Pütz Yann Quidé Anjanibhargavi Ragothaman Faisal M Rashid William R Reay Ronny Redlich Céline S Reinbold Jonathan Repple Geneviève Richard Brandalyn C Riedel Shannon L Risacher Cristiane S Rocha Nina Roth Mota Lauren Salminen Arvin Saremi Andrew J Saykin Fenja Schlag Lianne Schmaal Peter R Schofield Rodrigo Secolin Chin Yang Shapland Li Shen Jean Shin Elena Shumskaya Ida E Sønderby Emma Sprooten Katherine E Tansey Alexander Teumer Anbupalam Thalamuthu Diana Tordesillas-Gutiérrez Jessica A Turner Anne Uhlmann Costanza Ludovica Vallerga Dennis van der Meer Marjolein M J van Donkelaar Liza van Eijk Theo G M van Erp Neeltje E M van Haren Daan van Rooij Marie-José van Tol Jan H Veldink Ellen Verhoef Esther Walton Mingyuan Wang Yunpeng Wang Joanna M Wardlaw Wei Wen Lars T Westlye Christopher D Whelan Stephanie H Witt Katharina Wittfeld Christiane Wolf Thomas Wolfers Jing Qin Wu Clarissa L Yasuda Dario Zaremba Zuo Zhang Marcel P Zwiers Eric Artiges Amelia A Assareh Rosa Ayesa-Arriola Aysenil Belger Christine L Brandt Gregory G Brown Sven Cichon Joanne E Curran Gareth E Davies Franziska Degenhardt Michelle F Dennis Bruno Dietsche Srdjan Djurovic Colin P Doherty Ryan Espiritu Daniel Garijo Yolanda Gil Penny A Gowland Robert C Green Alexander N Häusler Walter Heindel Beng-Choon Ho Wolfgang U Hoffmann Florian Holsboer Georg Homuth Norbert Hosten Clifford R Jack MiHyun Jang Andreas Jansen Nathan A Kimbrel Knut Kolskår Sanne Koops Axel Krug Kelvin O Lim Jurjen J Luykx Daniel H Mathalon Karen A Mather Venkata S Mattay Sarah Matthews Jaqueline Mayoral Van Son Sarah C McEwen Ingrid Melle Derek W Morris Bryon A Mueller Matthias Nauck Jan E Nordvik Markus M Nöthen Daniel S O'Leary Nils Opel Marie-Laure Paillère Martinot G Bruce Pike Adrian Preda Erin B Quinlan Paul E Rasser Varun Ratnakar Simone Reppermund Vidar M Steen Paul A Tooney Fábio R Torres Dick J Veltman James T Voyvodic Robert Whelan Tonya White Hidenaga Yamamori Hieab H H Adams Joshua C Bis Stephanie Debette Charles Decarli Myriam Fornage Vilmundur Gudnason Edith Hofer M Arfan Ikram Lenore Launer W T Longstreth Oscar L Lopez Bernard Mazoyer Thomas H Mosley Gennady V Roshchupkin Claudia L Satizabal Reinhold Schmidt Sudha Seshadri Qiong Yang Marina K M Alvim David Ames Tim J Anderson Ole A Andreassen Alejandro Arias-Vasquez Mark E Bastin Bernhard T Baune Jean C Beckham John Blangero Dorret I Boomsma Henry Brodaty Han G Brunner Randy L Buckner Jan K Buitelaar Juan R Bustillo Wiepke Cahn Murray J Cairns Vince Calhoun Vaughan J Carr Xavier Caseras Svenja Caspers Gianpiero L Cavalleri Fernando Cendes Aiden Corvin Benedicto Crespo-Facorro John C Dalrymple-Alford Udo Dannlowski Eco J C de Geus Ian J Deary Norman Delanty Chantal Depondt Sylvane Desrivières Gary Donohoe Thomas Espeseth Guillén Fernández Simon E Fisher Herta Flor Andreas J Forstner Clyde Francks Barbara Franke David C Glahn Randy L Gollub Hans J Grabe Oliver Gruber Asta K Håberg Ahmad R Hariri Catharina A Hartman Ryota Hashimoto Andreas Heinz Frans A Henskens Manon H J Hillegers Pieter J Hoekstra Avram J Holmes L Elliot Hong William D Hopkins Hilleke E Hulshoff Pol Terry L Jernigan Erik G Jönsson René S Kahn Martin A Kennedy Tilo T J Kircher Peter Kochunov John B J Kwok Stephanie Le Hellard Carmel M Loughland Nicholas G Martin Jean-Luc Martinot Colm McDonald Katie L McMahon Andreas Meyer-Lindenberg Patricia T Michie Rajendra A Morey Bryan Mowry Lars Nyberg Jaap Oosterlaan Roel A Ophoff Christos Pantelis Tomas Paus Zdenka Pausova Brenda W J H Penninx Tinca J C Polderman Danielle Posthuma Marcella Rietschel Joshua L Roffman Laura M Rowland Perminder S Sachdev Philipp G Sämann Ulrich Schall Gunter Schumann Rodney J Scott Kang Sim Sanjay M Sisodiya Jordan W Smoller Iris E Sommer Beate St Pourcain Dan J Stein Arthur W Toga Julian N Trollor Nic J A Van der Wee Dennis van 't Ent Henry Völzke Henrik Walter Bernd Weber Daniel R Weinberger Margaret J Wright Juan Zhou Jason L Stein Paul M Thompson Sarah E Medland

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.

Neuroimage Clin 2020 6;26:102208. Epub 2020 Feb 6.

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis. Electronic address:

This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.
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http://dx.doi.org/10.1016/j.nicl.2020.102208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025090PMC
February 2020

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.

Neuroimage Clin 2020 6;26:102208. Epub 2020 Feb 6.

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis. Electronic address:

This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.
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http://dx.doi.org/10.1016/j.nicl.2020.102208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025090PMC
February 2020

Reproducibility of a ramping protocol to measure cerebral vascular reactivity using functional magnetic resonance imaging.

Clin Physiol Funct Imaging 2020 May 16;40(3):183-189. Epub 2020 Feb 16.

School of Kinesiology, University of Minnesota, Minneapolis, Minnesota.

Though individual differences in arterial carbon dioxide and oxygen levels inherently exist, the degree of their influence on cerebral vascular reactivity (CVR) is less clear. We examined the reproducibility of BOLD signal changes to an iso-oxic ramping P CO protocol. CVR changes were induced by altering P CO while holding P O constant using a computer-controlled sequential gas delivery (SGD) device. Two MRI scans, each including a linear change in P CO , were performed using a 3-Tesla (3T) scanner. This ramp sequence consisted of 1 min at 30 mmHg followed by 4 min period during where P CO was linearly increased from 30 to 50 mmHg, 1 min at 51 mmHg, and concluded with 4 min at baseline. The protocol was repeated at a separate visit with 3 days between visits (minimum). Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify reproducibility. Eleven subjects (6 females; mean age 26.5 ± 5.7 years) completed the full testing protocol. Good reproducibility was observed for the within-visit ramp sequence (Visit 1: ICC = 0.82, CV = 6.5%; Visit 2: ICC = 0.74, CV = 6.4%). Similarly, ramp sequence were reproducible between visits (Scan 1: ICC = 0.74, CV = 6.5%; Scan 2: ICC = 0.66, CV = 6.1%). Establishing reproducible methodologies for measuring BOLD signal changes in response to P CO alterations using a ramp protocol will allow researchers to study CVR functionality. Finally, adding a ramping protocol to CVR studies could provide information about changes in CVR over a broad range of P CO .
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http://dx.doi.org/10.1111/cpf.12621DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7131875PMC
May 2020

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time.

J Neurosci Methods 2020 Jan 21;334:108600. Epub 2020 Jan 21.

The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA; Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA; Department of Psychology, Georgia State University, GA, USA; Department of ECE, University of New Mexico, NM, USA.

Background: Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.

New Method: Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.

Results: Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.

Comparison With Existing Methods: We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.

Conclusions: As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.
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http://dx.doi.org/10.1016/j.jneumeth.2020.108600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371494PMC
January 2020

Classification of Major Depressive Disorder from Resting-State fMRI.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:3511-3514

Major Depressive Disorder (MDD) is a very serious mental illness that can affect the daily lives of patients. Accurate diagnosis of this disorder is necessary for planning individualized treatment. However, diagnosing MDD requires the clinicians to personally interview the subjects and rate the symptoms based on Diagnostic and Statistical Manual of Mental Disorders (DSM), which can be very time consuming. Discovering quantifiable signals and biomarkers associated with MDD using functional magnetic resonance imaging (fMRI) scans of patients have the potential to assist the clinicians in their assessment. This paper explores the use of resting-state functional connectivity and network features to classify MDD vs. healthy subjects. For each subject, mean time-series are extracted from 85 brain regions and they are decomposed to 4-frequency bands. Mean time-series for each of the frequency bands are utilized to compute the Pearson correlation and network characteristics. Features are selected separately from correlation and network characteristics using Minimum Redundancy Maximum Relevance (mRMR) to create the final classifier. The proposed scheme achieves 79% accuracy (65 out of 82 subjects classified correctly) with 86% sensitivity (42 out of 49 MDD subjects identified correctly) and 70% specificity (23 out of 33 controls identified correctly) using leave-one-out classification with in-fold feature selection. Pearson correlation had the highest discrimination in band 0.015-0.03 Hz and network based features had the highest discrimination in band 0.03-0.06 Hz for distinguishing MDD vs. healthy subjects.
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http://dx.doi.org/10.1109/EMBC.2019.8856453DOI Listing
July 2019

In-vivo cortical thickness estimation from high-resolution Tw MRI scans in healthy and mucopolysaccharidosis affected dogs.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2848-2851

Cortical thickness measurement estimated from high-resolution anatomical MRI scans may serve as a marker of cortical atrophy in clinical research applications. Most of the working algorithms and pipelines are optimized for human in-vivo data analyses that offer robust and reproducible measures. As animal-models are widely utilized in many preclinical phases of clinical trials the need for an optimized automated MRI data analysis to yield reliable data is warranted. We present a processing pipeline optimized for cortical thickness estimation of canine brains in native and template spaces. Preliminary results of 5 healthy and 5 mucopolysaccharidosis (MPS) dogs demonstrate single-canine mean/median cortical thickness in range of 2.69-3.58mm in native space and 3.26-4.15mm in template space. Our MRI generated values exceed previous histological measurements (observed mean about 2mm) in limited literature reports. Randomly selected manual measures corroborated the ranges defined by estimated cortical thickness probability density functions. Geometric transformations between native and template spaces change absolute mean/median cortical thickness values, but do not change the data nature and properties since the Pearson correlation coefficients between different space estimates were 0.84 for mean values and 0.89 for median values. No significant difference in total cortical thickness between MPS and age-and gender-matched dogs was observed.
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http://dx.doi.org/10.1109/EMBC.2019.8856826DOI Listing
July 2019

Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.

IEEE Trans Biomed Eng 2020 09 7;67(9):2572-2584. Epub 2020 Jan 7.

Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).

Methods: We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.

Results: Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.

Conclusion: We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.

Significance: The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
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http://dx.doi.org/10.1109/TBME.2020.2964724DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538162PMC
September 2020

Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.

IEEE Trans Biomed Eng 2020 09 7;67(9):2572-2584. Epub 2020 Jan 7.

Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).

Methods: We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.

Results: Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.

Conclusion: We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.

Significance: The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
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http://dx.doi.org/10.1109/TBME.2020.2964724DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538162PMC
September 2020

Oxytocin Enhances an Amygdala Circuit Associated With Negative Symptoms in Schizophrenia: A Single-Dose, Placebo-Controlled, Crossover, Randomized Control Trial.

Schizophr Bull 2020 04;46(3):661-669

Mental Health Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA.

Negative symptoms are core contributors to vocational and social deficits in schizophrenia (SZ). Available antipsychotic medications typically fail to reduce these symptoms. The neurohormone oxytocin (OT) is a promising treatment for negative symptoms, given its role in complex social behaviors mediated by the amygdala. In sample 1, we used a double-blind, placebo-controlled, crossover design to test the effects of a single dose of intranasal OT on amygdala resting-state functional connectivity (rsFC) in SZ (n = 22) and healthy controls (HC, n = 24) using a whole-brain corrected approach: we identified regions for which OT modulated SZ amygdala rsFC, assessed whether OT-modulated circuits were abnormal in SZ relative to HC on placebo, and evaluated whether connectivity on placebo and OT-induced connectivity changes correlated with baseline negative symptoms in SZ. Given our modest sample size, we used a second SZ (n = 183) and HC (n = 178) sample to replicate any symptom correlations. In sample 1, OT increased rsFC between the amygdala and left middle temporal gyrus, superior temporal sulcus, and angular gyrus (MTG/STS/AngG) in SZ compared to HC. Further, SZ had hypo-connectivity in this circuit compared to HC on placebo. More severe negative symptoms correlated with less amygdala-to-left-MTG/STS/AngG connectivity on placebo and with greater OT-induced connectivity increases. In sample 2, we replicated the correlation between amygdala-left-MTG/STS/AngG hypo-connectivity and negative symptoms, finding a specific association with expressive negative symptoms. These data suggest intranasal OT can normalize functional connectivity in an amygdala-to-left-MTG/STS/AngG circuit that contributes to negative symptoms in SZ.
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http://dx.doi.org/10.1093/schbul/sbz091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147578PMC
April 2020

Neurite orientation dispersion and density imaging quantifies corticospinal tract microstructural organization in children with unilateral cerebral palsy.

Hum Brain Mapp 2019 12 29;40(17):4888-4900. Epub 2019 Jul 29.

Division of Physical Therapy, Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, Minnesota.

Children with unilateral cerebral palsy (UCP) due to early brain injury exhibit disrupted connectivity of corticospinal tracts (CSTs), which can be quantified using diffusion-weighted magnetic resonance imaging (DWI). Diffusion tensor imaging (DTI) is commonly used to quantify white matter organization, however, this model lacks the biological specificity to accurately describe underlying microstructural properties. Newer approaches, such as neurite orientation dispersion and density imaging (NODDI), may provide more biologically accurate information regarding CST microstructure. In this study, we directly compared metrics of CST microstructure using NODDI and DTI models to characterize the microstructural organization of corticospinal pathways. Twenty participants with UCP participating in a neuromodulation/rehabilitation intervention underwent imaging including multi-shell DWI; 10 participants' datasets were adequately completed for neuroimaging analysis. Task fMRI-guided probabilistic tractography from motor cortex to brainstem was performed at baseline and follow-up to reconstruct the CSTs. Diffusion metrics were compared between hemispheres at baseline, and between baseline and follow-up to test for intervention effects. Correlation analyses were used to compare baseline metrics to changes in hand function following the intervention. DTI results showed that mean fractional anisotropy in lesioned and nonlesioned CSTs did not significantly differ, but mean, axial, and radial diffusivity were greater in the lesioned CST. For NODDI, intracellular volume fraction (ICVF) and orientation dispersion index (ODI) were lower in the lesioned CST. Unimanual function was strongly correlated with ICVF, but not FA. NODDI may reveal distinct properties of CST microstructure that are linked to motor function, indicating their potential in characterizing brain structure and development.
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http://dx.doi.org/10.1002/hbm.24744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813864PMC
December 2019

No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis.

Am J Psychiatry 2019 12 29;176(12):1039-1049. Epub 2019 Jul 29.

The Department of Language and Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands (de Kovel, Francks); Orygen, the National Centre of Excellence in Youth Mental Health, Melbourne, Australia (Davey); the Department of Psychiatry, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam (Veltman); the Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey (Jahanshad, Thompson); the Laboratory of Affective, Cognitive, and Translational Neuroscience, Scientific Research Institute of Physiology and Basic Medicine, Novosibirsk, Russian Federation (Aftanas, Brack, Osipov); the Department of Neuroscience, Neuroimaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (Aleman); the Department of Psychiatry, University of Melbourne, Melbourne (Baune); the Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany (Bülow); the Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil (Busatto Filho, Rosa); the Department of Psychiatry, Trinity College Dublin (Carballedo, Frodl); the Department of Psychiatry and the Weill Institute for Neurosciences, Division of Child and Adolescent Psychiatry, University of California, San Francisco (Connolly, Ho, Yang); the Department of Psychiatry, University of Minnesota Medical School, Minneapolis (Cullen, Mueller, Ubani, Schreiner); the Department of Psychiatry, University of Münster, Münster, Germany (Dannlowski, Dohm, Grotegerd, Leehr, Sindermann, Winter, Zaremba); the Department of Psychology, School of Arts and Social Sciences, City, University of London, London (Dima); the Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany (Erwin-Grabner; Goya-Maldonado, Schnell, Singh); the Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany (Frodl); the Centre for Affective Disorders, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Fu); the Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Canada (Hall); the Department of Psychiatry, Yale School of Medicine, New Haven, Conn. (Alexander-Bloch, Glahn); the Psychopharmacology Research Unit, Department of Psychiatry, University of Oxford, Oxford, U.K. (Godlewska); the Department of Psychology, Stanford University, Stanford, Calif. (Gotlib, Ho); the Department of Psychiatry and Psychotherapy, University Medicine Greifswald (Grabe, Wittfeld); the Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, University Medical Center Groningen (Groenewold); the Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany (Gruber, Krämer, Simulionyte); the Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, and Melbourne Health, Melbourne (Harrison); the Youth Mental Health Team, Brain and Mind Centre, University of Sydney, Sydney, Australia (Hatton, Hickie, Lagopoulos); the Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany (Kircher, Krug, Nenadic, Yüksel); the Department of Neurology, University of Magdeburg, Magdeburg (Li); the Departments of Psychiatry and Paediatrics, University of Calgary, Calgary, Canada (MacMaster, McLellan); the Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary (MacQueen); the Division of Psychiatry, University of Edinburgh, Edinburgh (Harris, McIntosh, Papmeyer, Whalley); Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia (Medland); the Department. of Psychiatry, Institute of Biomedical Research Sant Pau, Barcelona, Spain (Portella); the Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam (Reneman, Schrantee); the Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford (Sacchet); West Region and Research Division, Institute of Mental Health, Singapore (Sim); Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa (Groenewold, Stein); Brain Function and Dysfunction, Leids Universitair Medisch Centrum, Leiden, the Netherlands (Van der Wee); the Department of Psychiatry, Leiden University Medical Center, Leiden (Van der Werff); the Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin (Veer, H. Walter); the Institute of Information and Communication Technologies (Instituto ITACA), Universitat Politècnica de València, València, Spain (Gilabert); the Institute for Community Medicine, University Medicine Greifswald, Greifswald (Völzke); the Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany (M. Walter); the Department of Psychology, University of Minnesota, Minneapolis (Schreiner); the German Center for Neurodegenerative Diseases, Site Rostock/Greifswald (Grabe, Wittfeld); the Department of Neuroscience, Novosibirsk State University, Novosibirsk (Aftanas); the Department of Psychology, University of Groningen, Groningen (Aleman); the Center for Interdisciplinary Research on Applied Neurosciences, University of São Paulo, São Paulo (Busatto Filho, Rosa); the Department of Biomedical Sciences, Florida State University, Tallahassee (Connolly); the Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dima); the Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen (Francks); the School of Psychology, University of East London, London (Fu); the Sunshine Coast Mind and Neuroscience Thompson Institute, Queensland, Australia (Lagopoulos); Strategic Clinical Network for Addictions and Mental Health, Alberta, Canada (MacMaster); the Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh (McIntosh); the Rehabilitation Services and Care Unit, Swiss Paraplegic Research, Nottwil, Switzerland (Papmeyer); CIBERSAM, Madrid (Portella); the Centre for Youth Mental Health, University of Melbourne, Melbourne (Davey, Schmaal); the Spinoza Center for Neuroimaging, Royal Netherlands Academy of Arts and Sciences, Amsterdam (Schrantee); Yong Loo Lin School of Medicine, National University of Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore (Sim); the School of Public Health, Boston University, Boston (Ubani); the Leiden Institute for Brain and Cognition, Leiden (Van der Werff); the German Center for Cardiovascular Research, partner site Greifswald, Greifswald (Völzke).

Objective: Asymmetry is a subtle but pervasive aspect of the human brain, and it may be altered in several psychiatric conditions. MRI studies have shown subtle differences of brain anatomy between people with major depressive disorder and healthy control subjects, but few studies have specifically examined brain anatomical asymmetry in relation to this disorder, and results from those studies have remained inconclusive. At the functional level, some electroencephalography studies have indicated left fronto-cortical hypoactivity and right parietal hypoactivity in depressive disorders, so aspects of lateralized anatomy may also be affected. The authors used pooled individual-level data from data sets collected around the world to investigate differences in laterality in measures of cortical thickness, cortical surface area, and subcortical volume between individuals with major depression and healthy control subjects.

Methods: The authors investigated differences in the laterality of thickness and surface area measures of 34 cerebral cortical regions in 2,256 individuals with major depression and 3,504 control subjects from 31 separate data sets, and they investigated volume asymmetries of eight subcortical structures in 2,540 individuals with major depression and 4,230 control subjects from 32 data sets. T-weighted MRI data were processed with a single protocol using FreeSurfer and the Desikan-Killiany atlas. The large sample size provided 80% power to detect effects of the order of Cohen's d=0.1.

Results: The largest effect size (Cohen's d) of major depression diagnosis was 0.085 for the thickness asymmetry of the superior temporal cortex, which was not significant after adjustment for multiple testing. Asymmetry measures were not significantly associated with medication use, acute compared with remitted status, first episode compared with recurrent status, or age at onset.

Conclusions: Altered brain macro-anatomical asymmetry may be of little relevance to major depression etiology in most cases.
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http://dx.doi.org/10.1176/appi.ajp.2019.18101144DOI Listing
December 2019

Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations.

Front Neurosci 2019 27;13:634. Epub 2019 Jun 27.

Mind Research Network, Albuquerque, NM, United States.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.
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http://dx.doi.org/10.3389/fnins.2019.00634DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611425PMC
June 2019

Neurocircuitry associated with symptom dimensions at baseline and with change in borderline personality disorder.

Psychiatry Res Neuroimaging 2019 08 5;290:58-65. Epub 2019 Jul 5.

University of Minnesota Medical School, Department of Psychiatry, 2450 Riverside Avenue, Minneapolis, MN 55454, United States. Electronic address:

Borderline personality disorder (BPD) is a serious illness associated with chronic suffering and self-injurious behavior. Parsing the relationships between specific symptom domains and their underlying biological mechanisms may help us further understand the neural circuits implicated in these symptoms and how they might be amenable to change with treatment. This study examines the association between symptom dimensions (Affective Disturbance, Cognitive Disturbance, Disturbed Relationships, and Impulsivity) and amygdala resting-state functional connectivity (RSFC) in a sample of adults with BPD (n = 18). We also explored the relationships between change in symptom dimensions and change in amygdala RSFC in a subset of this sample (n = 13) following 8 weeks of quetiapine or placebo. At baseline, higher impulsivity was associated with increased positive RSFC between right amygdala and left hippocampus. There were no significant differences in neural change between treatment groups. Improvement in cognitive disturbance was associated with increased positive RSFC between left amygdala and temporal fusiform and parahippocampal gyri. Improvement in disturbed relationships was associated with increased negative RSFC between right amygdala and frontal pole. These results support that specific dimensions of BPD are associated with specific neural connectivity patterns at baseline and with change, which may represent neural treatment targets.
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http://dx.doi.org/10.1016/j.pscychresns.2019.07.001DOI Listing
August 2019

Dentate gyrus volume deficit in schizophrenia.

Psychol Med 2020 06 3;50(8):1267-1277. Epub 2019 Jun 3.

Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA92617, USA.

Background: Schizophrenia is associated with robust hippocampal volume deficits but subregion volume deficits, their associations with cognition, and contributing genes remain to be determined.

Methods: Hippocampal formation (HF) subregion volumes were obtained using FreeSurfer 6.0 from individuals with schizophrenia (n = 176, mean age ± s.d. = 39.0 ± 11.5, 132 males) and healthy volunteers (n = 173, mean age ± s.d. = 37.6 ± 11.3, 123 males) with similar mean age, gender, handedness, and race distributions. Relationships between the HF subregion volume with the largest between group difference, neuropsychological performance, and single-nucleotide polymorphisms were assessed.

Results: This study found a significant group by region interaction on hippocampal subregion volumes. Compared to healthy volunteers, individuals with schizophrenia had significantly smaller dentate gyrus (DG) (Cohen's d = -0.57), Cornu Ammonis (CA) 4, molecular layer of the hippocampus, hippocampal tail, and CA 1 volumes, when statistically controlling for intracranial volume; DG (d = -0.43) and CA 4 volumes remained significantly smaller when statistically controlling for mean hippocampal volume. DG volume showed the largest between group difference and significant positive associations with visual memory and speed of processing in the overall sample. Genome-wide association analysis with DG volume as the quantitative phenotype identified rs56055643 (β = 10.8, p < 5 × 10-8, 95% CI 7.0-14.5) on chromosome 3 in high linkage disequilibrium with MOBP. Gene-based analyses identified associations between SLC25A38 and RPSA and DG volume.

Conclusions: This study suggests that DG dysfunction is fundamentally involved in schizophrenia pathophysiology, that it may contribute to cognitive abnormalities in schizophrenia, and that underlying biological mechanisms may involve contributions from MOBP, SLC25A38, and RPSA.
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http://dx.doi.org/10.1017/S0033291719001144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068799PMC
June 2020

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia.

Hum Brain Mapp 2019 09 16;40(13):3795-3809. Epub 2019 May 16.

The Mind Research Network, Albuquerque, New Mexico.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
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http://dx.doi.org/10.1002/hbm.24632DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865807PMC
September 2019