scholarly journals Data-driven parcellation and graph theory analyses to study adolescent mood and anxiety symptoms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Benjamin A. Ely ◽  
Qi Liu ◽  
Samuel J. DeWitt ◽  
Lushna M. Mehra ◽  
Carmen M. Alonso ◽  
...  
Keyword(s):  
Graph Theory ◽  
Resting State ◽  
Ventral Striatum ◽  
Psychiatric Symptoms ◽  
Clinical Status ◽  
Anterior Cingulate ◽  
Data Driven ◽  
High Quality ◽  
Human Connectome Project ◽  
Psychiatric Conditions

AbstractAdolescence is a period of rapid brain development when psychiatric symptoms often first emerge. Studying adolescents may therefore facilitate the identification of neural alterations early in the course of psychiatric conditions. Here, we sought to utilize new, high-quality brain parcellations and data-driven graph theory approaches to characterize associations between resting-state networks and the severity of depression, anxiety, and anhedonia symptoms—salient features across psychiatric conditions. As reward circuitry matures considerably during adolescence, we examined both Whole Brain and three task-derived reward networks. Subjects were 87 psychotropic-medication-free adolescents (age = 12–20) with diverse psychiatric conditions (n = 68) and healthy controls (n = 19). All completed diagnostic interviews, dimensional clinical assessments, and 3T resting-state fMRI (10 min/2.3 mm/TR = 1 s). Following high-quality Human Connectome Project-style preprocessing, multimodal surface matching (MSMAll) alignment, and parcellation via the Cole-Anticevic Brain-wide Network Partition, weighted graph theoretical metrics (Strength Centrality = CStr; Eigenvector Centrality = CEig; Local Efficiency = ELoc) were estimated within each network. Associations with symptom severity and clinical status were assessed non-parametrically (two-tailed pFWE < 0.05). Across subjects, depression scores correlated with ventral striatum CStr within the Reward Attainment network, while anticipatory anhedonia correlated with CStr and ELoc in the subgenual anterior cingulate, dorsal anterior cingulate, orbitofrontal cortex, caudate, and ventral striatum across multiple networks. Group differences and associations with anxiety were not detected. Using detailed functional and clinical measures, we found that adolescent depression and anhedonia involve increased influence and communication efficiency in prefrontal and limbic reward areas. Resting-state network properties thus reflect positive valence system anomalies related to discrete reward sub-systems and processing phases early in the course of illness.

scholarly journals Modulation of Resting Connectivity Between the Mesial Frontal Cortex and Basal Ganglia

2018 ◽  
Author(s):  
Traian Popa ◽  
Laurel S. Morris ◽  
Rachel Hunt ◽  
Zhi-De Deng ◽  
Silvina Horovitz ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Ventral Striatum ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Unexpected Finding ◽  
Independent Components Analysis ◽  
Independent Components ◽  
Components Analysis

AbstractThe mesial prefrontal cortex, cingulate cortex and the ventral striatum are key nodes of the human mesial fronto-striatal circuit involved in decision-making and executive function and pathological disorders. Here we ask whether deep wide-field repetitive transcranial magnetic stimulation (rTMS) targeting the mesial prefrontal cortex (MPFC) influences resting state functional connectivity. In Study 1, we examined functional connectivity using resting state multi-echo and independent components analysis in 154 healthy subjects to characterize default connectivity in the MPFC and mid-cingulate cortex (MCC). In Study 2, we used inhibitory, 1 Hz deep rTMS with the H7-coil targeting MPFC and dorsal anterior cingulate (dACC) in a separate group of 20 healthy volunteers and examined pre-and post-TMS functional connectivity using seed-based and independent components analysis. In Study 1, we show that MPFC and MCC have distinct patterns of functional connectivity with MPFC–ventral striatum showing negative, whereas MCC–ventral striatum showing positive functional connectivity. Low-frequency rTMS decreased functional connectivity of MPFC and dACC with the ventral striatum. We further showed enhanced connectivity between MCC and ventral striatum. These findings emphasize how deep inhibitory rTMS using the H7-coil can influence underlying network functional connectivity by decreasing connectivity of the targeted MPFC regions, thus potentially enhancing response inhibition and decreasing drug cue reactivity processes relevant to addictions. The unexpected finding of enhanced default connectivity between MCC and ventral striatum may be related to the decreased influence and connectivity between the MPFC and MCC. These findings are highly relevant to the treatment of disorders relying on the mesioprefrontal–cingulo–striatal circuit.

Remitted major depression is related to increased functional coupling between ventral striatum and cortical regions in resting state fMRI

2011 ◽  
Vol 26 (S2) ◽  
pp. 948-948 ◽  
Author(s):  
G. Pail ◽  
C. Scharinger ◽  
K. Kalcher ◽  
W. Huf ◽  
R. Boubela ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Orbitofrontal Cortex ◽  
Resting State ◽  
Ventral Striatum ◽  
Resting State Fmri ◽  
Anterior Cingulate ◽  
Functional Coupling ◽  
Healthy Controls ◽  
Full Remission ◽  
Cortical Regions

IntroductionDysfunction in the basal ganglia has been related to impaired reward processing and anhedonia, a core symptom of Major Depressive Disorder (MDD). In particular, the ventral striatum including the nucleus accumbens is increasingly implicated in the pathophysiology of MDD, but evidence for a specific role during episodes of full remission is lacking so far.ObjectivesTo investigate functional connectivity patterns of resting-state activity in patients in the remitted phase of MDD (rMDD).AimsTo determine whether rMDD is related to disruptions of functional coupling between the ventral striatum and cortical regions.MethodsForty-three remitted depressed patients and thirty-five healthy controls were recruited at Medical University of Vienna, Vienna, Austria, and performed a six minute resting-state fMRI scan. Seed time series were extracted from the preprocessed data using individual masks for ventral striatum and correlated with all nodes in a surface based analysis using FreeSurfer, AFNI and SUMA. The resulting correlation coefficients were then Fishertransformed, group results were determined by comparing group mean smoothed z-scores with a two-sample ttest.ResultsIncreased resting-state functional connectivity was revealed between ventral striatum (seed region) and anterior cingulate cortex as well as orbitofrontal cortex in the rMDD group compared to healthy controls.ConclusionsOur preliminary data is in accordance with the idea that increased functional coupling between the ventral striatum and two major emotion processing regions, the anterior cingulate cortex and the orbitofrontal cortex, may represent a neural mechanism contributing to the maintenance of full remission of MDD.

scholarly journals The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

2019 ◽  
Author(s):  
Sean P. Fitzgibbon ◽  
Samuel J. Harrison ◽  
Mark Jenkinson ◽  
Luke Baxter ◽  
Emma C. Robinson ◽  
...  
Keyword(s):  
Resting State ◽  
Motion Correction ◽  
Substantial Reduction ◽  
Distortion Correction ◽  
Fmri Data ◽  
Dynamic Susceptibility ◽  
List Type ◽  
High Quality ◽  
Multimodal Registration ◽  
Human Connectome Project

AbstractThe developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.HighlightsAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI dataIncludes integrated dynamic distortion and slice-to-volume motion correctionA robust multimodal registration approach which includes custom neonatal templatesIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspectionData analysis of 538 infants imaged at 26-45 weeks post-menstrual age

Modulation of salience network intranetwork resting state functional connectivity in women with chronic migraine

Cephalalgia ◽  
2017 ◽  
Vol 38 (11) ◽  
pp. 1731-1741 ◽  
Author(s):  
X Michelle Androulakis ◽  
Chris Rorden ◽  
B Lee Peterlin ◽  
Kaitlin Krebs
Keyword(s):  
Prefrontal Cortex ◽  
Chronic Migraine ◽  
Resting State ◽  
Ventral Striatum ◽  
Medication Overuse ◽  
Medication Overuse Headache ◽  
Anterior Cingulate ◽  
Salience Network ◽  
Dorsal Anterior Cingulate Cortex ◽  
Extended Amygdala

Objective To investigate the intranetwork resting state fMRI connectivity within the Salience Network of chronic migraine with and without medication overuse headache. Methods We compared 351 pairs of intranetwork connectivity in chronic migraine (n = 13) and chronic migraine with medication overuse headache (n = 16) compared to matched controls, and between each chronic migraine subgroup. Results Compared to controls, 17 pairs of intranetwork connections in chronic migraine and 27 pairs in chronic migraine with medication overuse headache were decreased. When comparing chronic migraine with medication overuse headache versus chronic migraine, connectivity between bilateral extended amygdala, and between paracingulate to right ventral tegmental area/substantia nigra were decreased in chronic migraine (chronic migraine < chronic migraine with medication overuse headache). Connectivity between left dorsolateral prefrontal cortex to bilateral ventral striatum/pallidum, to bilateral dorsal anterior cingulate cortex; left anterior prefrontal cortex to contralateral orbitofrontal insula; and left ventral striatum/pallidum to ipsilateral supplementary motor area (SMA)/preSMA were decreased in chronic migraine with medication overuse headache (chronic migraine with medication overuse headache < chronic migraine). Conclusion Both chronic migraine subgroups had shared intranetwork connectivity abnormality, however, each subgroup had unique pattern of disruption within the salience network. The results suggest that the aberrant assignment of salience to external and internal stimuli plays an important role in chronic migraine and chronic migraine with medication overuse headache interictally, mostly involving mesolimbic pathways (especially bilateral extended amygdala) in chronic migraine, and prefrontal-subcortical limbic pathways in chronic migraine with medication overuse headache.

scholarly journals Exploring the Neuroplastic Effects of Biofeedback Training on Smokers

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Niki Pandria ◽  
Alkinoos Athanasiou ◽  
Nikos Terzopoulos ◽  
Evangelos Paraskevopoulos ◽  
Maria Karagianni ◽  
...  
Keyword(s):  
Resting State ◽  
Psychiatric Symptoms ◽  
Brain Plasticity ◽  
Clinical Status ◽  
Protective Role ◽  
Nicotine Addiction ◽  
Biofeedback Training ◽  
Resting State Functional Connectivity ◽  
Before And After ◽  
The Right

Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.

scholarly journals Neural Correlates of Positive and Negative Valence System Dysfunction in Adolescents Revealed by Data-Driven Parcellation and Resting-State Network Modeling

2020 ◽  
Author(s):  
Vilma Gabbay ◽  
Qi Liu ◽  
Samuel J. DeWitt ◽  
Lushna M. Mehra ◽  
Carmen M. Alonso ◽  
...  
Keyword(s):  
Resting State ◽  
Clinical Status ◽  
Data Driven ◽  
Eigenvector Centrality ◽  
Whole Brain ◽  
Illness Onset ◽  
Negative Valence ◽  
Resting State Network ◽  
Wide Range ◽  
Positive And Negative Valence

AbstractObjectiveAdolescence is a period of rapid brain development when symptoms of mood, anxiety, and other disorders often first emerge, suggesting disruptions in maturing reward circuitry may play a role in mental illness onset. Here, we characterized associations between resting-state network properties and psychiatric symptomatology in medication-free adolescents with a wide range of symptom severity.MethodsAdolescents (age 12-20) with mood and/or anxiety symptoms (n=68) and healthy controls (n=19) completed diagnostic interviews, depression/anhedonia/anxiety questionnaires, and 3T resting-state fMRI (10min/2.3mm/TR=1s). Data were preprocessed (HCP Pipelines), aligned (MSMAll), and parcellated into 750 nodes encompassing the entire cortex/subcortex (Cole-Anticevic Brain-wide Network Partition). Weighted graph theoretical metrics (Strength Centrality=CStr; Eigenvector Centrality=CEig; Local Efficiency=ELoc) were estimated within Whole Brain and task-derived Reward Anticipation/Attainment/Prediction Error networks. Associations with clinical status and symptoms were assessed non-parametrically (two-tailed pFWE<0.05).ResultsRelative to controls, clinical adolescents had increased ventral striatum CEig within the Reward Attainment network. Across subjects, depression correlated with subgenual cingulate CStr and ELoc, anhedonia correlated with ventromedial prefrontal CStr and lateral amygdala ELoc, and anxiety negatively correlated with parietal operculum CEig and medial amygdala ELoc within the Whole Brain network.ConclusionsUsing a data-driven analysis approach, high-quality parcellation, and clinically diverse adolescent cohort, we found that symptoms within positive and negative valence system constructs differentially associated with resting-state network abnormalities: depression and anhedonia, as well as clinical status, involved greater influence and communication efficiency in prefrontal and limbic reward areas, whereas anxiety was linked to reduced influence/efficiency in amygdala and cortical regions involved in stimulus monitoring.

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

2021 ◽  
pp. 1-10
Author(s):  
Daniel M. Stout ◽  
Katia M. Harlé ◽  
Sonya B. Norman ◽  
Alan N. Simmons ◽  
Andrea D. Spadoni
Keyword(s):  
Alcohol Use ◽  
Resting State ◽  
Alcohol Use Disorder ◽  
Prolonged Exposure ◽  
Exposure Therapy ◽  
Anterior Cingulate ◽  
Data Driven ◽  
Future Research ◽  
Midcingulate Cortex ◽  
Prolonged Exposure Therapy

Abstract Background Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy. Methods In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment. Results We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption. Conclusions Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions – setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

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