New resting-state fMRI related studies at PubMed

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Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

Wed, 01/23/2019 - 18:00
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Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

Cereb Cortex. 2019 Jan 21;:

Authors: Pijnenburg R, Scholtens LH, Mantini D, Vanduffel W, Barrett LF, van den Heuvel MP

Abstract
Functional connectivity is defined as the statistical dependency of neurophysiological activity between 2 separate brain areas. To investigate the biological characteristics of resting-state functional connectivity (rsFC)-and in particular the significance of connection-wise variation in time-series correlations-rsFC was compared with strychnine-based connectivity measured in the macaque. Strychnine neuronography is a historical technique that induces activity in cortical areas through means of local administration of the substance strychnine. Strychnine causes local disinhibition through GABA suppression and leads to subsequent activation of functional pathways. Multiple resting-state fMRI recordings were acquired in 4 macaques (examining in total 299 imaging runs) from which a group-averaged rsFC matrix was constructed. rsFC was observed to be higher (P < 0.0001) between region-pairs with a strychnine-based connection as compared with region-pairs with no strychnine-based connection present. In particular, higher resting-state connectivity was observed in connections that were relatively stronger (weak < moderate < strong; P < 0.01) and in connections that were bidirectional (P < 0.0001) instead of unidirectional in strychnine-based connectivity. Our results imply that the level of correlation between brain areas as extracted from resting-state fMRI relates to the strength of underlying interregional functional pathways.

PMID: 30668705 [PubMed - as supplied by publisher]

Functional Brain Network Estimation with Time Series Self-scrubbing.

Wed, 01/23/2019 - 18:00
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Functional Brain Network Estimation with Time Series Self-scrubbing.

IEEE J Biomed Health Inform. 2019 Jan 18;:

Authors: Li W, Qiao L, Zhang L, Wang Z, Shen D

Abstract
Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g. blood oxygen level dependent signal series). In practice, a preprocessing pipeline is usually employed for improving data quality. With this in mind, some data points (volumes or time course in the time series) are still not clean enough, due to artifacts including spurious resting-state processes (head movement, mind-wandering). Therefore, not all volumes in the fMRI time series can contribute to the subsequent FBN estimation. To address this issue, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for jointly scrubbing the data and estimating FBN simultaneously. To further illustrate the effectiveness of the proposed method, we conduct experiments on two publicly datasets to identify subjects with mild cognitive impairment (MCI) from normal controls (NCs) based on the estimated FBNs, and achieve improved accuracies than the baseline methods.

PMID: 30668484 [PubMed - as supplied by publisher]

Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study.

Wed, 01/23/2019 - 18:00
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Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study.

Hum Brain Mapp. 2019 Jan 21;:

Authors: Shah P, Bassett DS, Wisse LEM, Detre JA, Stein JM, Yushkevich PA, Shinohara RT, Elliott MA, Das SR, Davis KA

Abstract
Mesial temporal lobe epilepsy (TLE) is a common neurological disorder affecting the hippocampus and surrounding medial temporal lobe (MTL). Although prior studies have analyzed whole-brain network distortions in TLE patients, the functional network architecture of the MTL at the subregion level has not been examined. In this study, we utilized high-resolution 7T T2-weighted magnetic resonance imaging (MRI) and resting-state BOLD-fMRI to characterize volumetric asymmetry and functional network asymmetry of MTL subregions in unilateral medically refractory TLE patients and healthy controls. We subdivided the TLE group into mesial temporal sclerosis patients (TLE-MTS) and MRI-negative nonlesional patients (TLE-NL). Using an automated multi-atlas segmentation pipeline, we delineated 10 MTL subregions per hemisphere for each subject. We found significantly different patterns of volumetric asymmetry between the two groups, with TLE-MTS exhibiting volumetric asymmetry corresponding to decreased volumes ipsilaterally in all hippocampal subfields, and TLE-NL exhibiting no significant volumetric asymmetries other than a mild decrease in whole-hippocampal volume ipsilaterally. We also found significantly different patterns of functional network asymmetry in the CA1 subfield and whole hippocampus, with TLE-NL patients exhibiting asymmetry corresponding to increased connectivity ipsilaterally and TLE-MTS patients exhibiting asymmetry corresponding to decreased connectivity ipsilaterally. Our findings provide initial evidence that functional neuroimaging-based network properties within the MTL can distinguish between TLE subtypes. High-resolution MRI has potential to improve localization of underlying brain network disruptions in TLE patients who are candidates for surgical resection.

PMID: 30666753 [PubMed - as supplied by publisher]

Modular preprocessing pipelines can reintroduce artifacts into fMRI data.

Wed, 01/23/2019 - 18:00
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Modular preprocessing pipelines can reintroduce artifacts into fMRI data.

Hum Brain Mapp. 2019 Jan 21;:

Authors: Lindquist MA, Geuter S, Wager TD, Caffo BS

Abstract
The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.

PMID: 30666750 [PubMed - as supplied by publisher]

Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study.

Wed, 01/23/2019 - 18:00
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Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study.

Front Neurosci. 2018;12:994

Authors: Chen J, Sun D, Shi Y, Jin W, Wang Y, Xi Q, Ren C

Abstract
Objective: To examine whether subacute stroke patients would exhibit abnormal dynamic characteristics of brain activity relative to healthy controls (HC) and to investigate whether the altered dynamic regional indexes were associated with clinical behavior in stroke patients. Methods: The dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic regional homogeneity (dReHo) in 42 subacute stroke patients and 55 healthy controls were compared. Correlation analyses between dALFF and dReHo in regions showing significant intergroup differences and clinical scores (i.e., the National Institutes of Health Stroke Scale, Fugl-Meyer assessment and lesion volume size) were conducted in stroke patients. Receiver operating characteristic (ROC) curve analysis was used to determine the potential value of altered dynamic regional indexes to identify stroke patients. Results: Significantly dALFF in the bilateral cerebellum posterior lobe (CPL), ipsilesional superior parietal lobe, ipsilesional inferior temporal gyrus (ITG), the midline supplementary motor area (SMA), ipsilesional putamen and lentiform nucleus were detected in stroke patients compared to HC. Relative to the HC group, the stroke patients showed significant differences in dReHo in the contralesional rectal gyrus, contralesional ITG, contralesional pons, ipsilesional middle frontal gyrus (MFG). Significant correlations between dALFF variability in midline SMA and Fugl-Meyer assessment (FMA) scores or between dReHo variability in the ipsilesional MFG and FMA scores were detected in stroke patients. Furthermore, the ROC curve revealed that dynamic ALFF at SMA and ReHo at ipsilesional MFG might have the potential to distinguish stroke patients. Conclusion: The pattern of intrinsic brain activity variability is altered in stroke patients compared with HC, and dynamic ALFF/ReHo might be potential tools to assess stroke patients' motor function.

PMID: 30666181 [PubMed]

Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI.

Tue, 01/22/2019 - 17:00

Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI.

Comput Biol Med. 2019 Jan 15;106:24-30

Authors: Chockanathan U, DSouza AM, Abidin AZ, Schifitto G, Wismüller A

Abstract
HIV-associated neurocognitive disorders (HAND) represent an important source of neurologic complications in individuals with HIV. The dynamic, often subclinical, course of HAND has rendered diagnosis, which currently depends on neuropsychometric (NP) evaluation, a challenge for clinicians. Here, we present evidence that functional brain connectivity, derived by large-scale Granger causality (lsGC) analysis of resting-state functional MRI (rs-fMRI) time-series, represents a potential biomarker to address this critical diagnostic need. Brain graph properties were used as features in machine learning tasks to 1) classify individuals as HIV+ or HIV- and 2) to predict overall cognitive performance, as assessed by NP scores, in a 22-subject (13 HIV-, 9 HIV+) cohort. Over nearly all seven brain parcellation templates considered, support vector machine (SVM) classifiers based on lsGC-derived brain graph features significantly outperformed those based on conventional Pearson correlation (PC)-derived features (p<0.05, Bonferroni-corrected). In a second task for which the objective was to predict the overall NP score of each subject, the lsGC-based SVM regressors consistently outperformed the PC-based regressors (p<0.05, Bonferroni-corrected) on nearly all templates. With the widely used Automated Anatomical Labeling (AAL90) template, it was determined that the brain regions that figured most strongly in the SVM classifiers included those of the default mode network (posterior cingulate cortex, angular gyrus) and basal ganglia (caudate nucleus), dysfunction in both of which have been observed in previous structural and functional analyses of HAND.

PMID: 30665138 [PubMed - as supplied by publisher]

Repetitive verbal behaviors are not always harmful signs: Compensatory plasticity within the language network in aphasia.

Tue, 01/22/2019 - 17:00

Repetitive verbal behaviors are not always harmful signs: Compensatory plasticity within the language network in aphasia.

Brain Lang. 2019 Jan 18;190:16-30

Authors: Torres-Prioris MJ, López-Barroso D, Roé-Vellvé N, Paredes-Pacheco J, Dávila G, Berthier ML

Abstract
Repetitive verbal behaviors such as conduite d'approche (CdA) and mitigated echolalia (ME) are well-known phenomena since early descriptions of aphasia. Nevertheless, there is no substantial fresh knowledge on their clinical features, neural correlates and treatment interventions. In the present study we take advantage of three index cases of chronic fluent aphasia showing CdA, ME or both symptoms to dissect their clinical and neural signatures. Using multimodal neuroimaging (structural magnetic resonance imaging and [18]-fluorodeoxyglucose positron emission tomography during resting state), we found that despite of the heterogeneous lesions in terms of etiology (stroke, traumatic brain injury), volume and location, CdA was present when the lesion affected in greater extent the left dorsal language pathway, while ME resulted from preferential damage to the left ventral stream. The coexistence of CdA and ME was associated with involvement of areas overlapping with the structural lesions and metabolic derangements described in the subjects who showed one of these symptoms (CdA or ME). These findings suggest that CdA and ME represent the clinical expression of plastic changes that occur within the spared language network and its interconnected areas in order to compensate for the linguistic functions that previously relied on the activity of the damaged pathway. We discuss the results in the light of this idea and consider alternative undamaged neural networks that may support CdA and ME.

PMID: 30665003 [PubMed - as supplied by publisher]

Dysconnectivity of the medio-dorsal thalamic nucleus in drug-naïve first episode schizophrenia: diagnosis-specific or trans-diagnostic effect?

Tue, 01/22/2019 - 17:00

Dysconnectivity of the medio-dorsal thalamic nucleus in drug-naïve first episode schizophrenia: diagnosis-specific or trans-diagnostic effect?

Transl Psychiatry. 2019 Jan 16;9(1):9

Authors: Gong Q, Puthusseryppady V, Dai J, He M, Xu X, Shi Y, Zhou B, Ai Y, Yang C, Zhang F, Lui S, Mechelli A

Abstract
Converging lines of evidence implicate the thalamocortical network in schizophrenia. In particular, the onset of the illness is associated with aberrant functional integration between the medio-dorsal thalamic nucleus (MDN) and widespread prefrontal, temporal and parietal cortical regions. Because the thalamus is also implicated in other psychiatric illnesses including post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), the diagnostic specificity of these alterations is unclear. Here, we determined whether aberrant functional integration between the MDN and the cortex is a specific feature of schizophrenia or a trans-diagnostic feature of psychiatric illness. Effective connectivity (EC) between the MDN and rest of the cortex was measured by applying psychophysiological interaction analysis to resting-state functional magnetic resonance imaging data of 50 patients with first episode schizophrenia (FES), 50 patients with MDD, 50 patients with PTSD and 122 healthy controls. All participants were medication-naïve. The only significant schizophrenia-specific effect was increased EC between the right MDN and the right pallidum (p < 0.05 corrected). In contrast, there were a number of significant trans-diagnostic alterations, with both right and left MDN displaying trans-diagnostic increased EC with several prefrontal and parietal regions bilaterally (p < 0.05 corrected). EC alterations between the MDN and the cortex are not specific to schizophrenia but are a trans-diagnostic feature of psychiatric disorders, consistent with emerging conceptualizations of mental illness based on a single general psychopathology factor. Therefore, dysconnectivity of the MDN could potentially be used to assess the presence of general psychopathology above and beyond traditional diagnostic boundaries.

PMID: 30664626 [PubMed - in process]

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network.

Tue, 01/22/2019 - 17:00

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network.

Hum Brain Mapp. 2019 Jan 21;:

Authors: Kottaram A, Johnston LA, Cocchi L, Ganella EP, Everall I, Pantelis C, Kotagiri R, Zalesky A

Abstract
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.

PMID: 30664285 [PubMed - as supplied by publisher]

Classification of schizophrenia by intersubject correlation in functional connectome.

Tue, 01/22/2019 - 17:00

Classification of schizophrenia by intersubject correlation in functional connectome.

Hum Brain Mapp. 2019 Jan 21;:

Authors: Ji GJ, Chen X, Bai T, Wang L, Wei Q, Gao Y, Tao L, He K, Li D, Dong Y, Hu P, Yu F, Zhu C, Tian Y, Yu Y, Wang K

Abstract
Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.

PMID: 30663853 [PubMed - as supplied by publisher]

The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization.

Tue, 01/22/2019 - 17:00
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The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization.

Front Physiol. 2018;9:1852

Authors: Chen Y, Liu YN, Zhou P, Zhang X, Wu Q, Zhao X, Ming D

Abstract
Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21-32 years), the adult (age 41-54 years), and the old (age 60-86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.

PMID: 30662409 [PubMed]

Maternal Adiposity Influences Neonatal Brain Functional Connectivity.

Tue, 01/22/2019 - 17:00
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Maternal Adiposity Influences Neonatal Brain Functional Connectivity.

Front Hum Neurosci. 2018;12:514

Authors: Salzwedel AP, Gao W, Andres A, Badger TM, Glasier CM, Ramakrishnaiah RH, Rowell AC, Ou X

Abstract
The neural mechanisms associated with obesity have been extensively studied, but the impact of maternal obesity on fetal and neonatal brain development remains poorly understood. In this study of full-term neonates, we aimed to detect potential neonatal functional connectivity alterations associated with maternal adiposity, quantified via body-mass-index (BMI) and body-fat-mass (BFM) percentage, based on seed-based and graph theoretical analysis using resting-state fMRI data. Our results revealed significant neonatal functional connectivity alterations in all four functional domains that are implicated in adult obesity: sensory cue processing, reward processing, cognitive control, and motor control. Moreover, some of the detected areas showing regional functional connectivity alterations also showed global degree and efficiency differences. These findings provide important clues to the potential neural basis for cognitive and mental health development in offspring of obese mothers and may lead to the derivation of imaging-based biomarkers for the early identification of risks for timely intervention.

PMID: 30662399 [PubMed]

Altered Functional Connectivity of Cerebello-Cortical Circuit in Multiple System Atrophy (Cerebellar-Type).

Tue, 01/22/2019 - 17:00
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Altered Functional Connectivity of Cerebello-Cortical Circuit in Multiple System Atrophy (Cerebellar-Type).

Front Neurosci. 2018;12:996

Authors: Ren S, Zhang H, Zheng W, Liu M, Gao F, Wang Z, Chen Z

Abstract
Multiple system atrophy (MSA) is regarded as a progressive neurodegenerative disease mainly divided into MSA-p type with Parkinsonism and MSA-c type with cerebellar ataxia as the main symptom. However, its neural mechanism is still unclear. In this study, we only focus on the MSA-c type. The purpose of this study is to explore the functional connectivity changes of the cerebello-cortical circuit in MSA-c type by using resting state functional magnetic resonance imaging (rs-fMRI). Thirty-six subjects (18 MSA and 18 normal controls) participated in this study and the rs-fMRI data were collected by applying resting state amplitude of low-frequency fluctuations (ALFF), we found the significant decreased ALFF in the MSA patients relative to controls, which included left cerebellum 8 area, 9 area, 7b area and Cru1 as well as vermis 7. Then we select the brain region of cerebellum 8 area as seed to investigate whole brain functional connectivity alteration in the MSA patients. When comparing to controls, several regions showed decreased connectivity in the MSA patients including bilateral cerebellum anterior lobe, left cerebellum posterior lobe, left dentate, bilateral pons, inferior parietal lobule (IPL), lingual gyrus (LG), parahippocampus (PHG), and middle temporal gyrus (MTG). In addition, there were closely correlation between functional connectivities and clinical performances in the MSA patients. The current study confirmed that the disrupted functional connectivity of specific cerebello-cortical circuit in the MSA patients, which is responsible for the clinical performances.

PMID: 30662394 [PubMed]

Structural and functional brain imaging in acute HIV.

Tue, 01/22/2019 - 17:00
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Structural and functional brain imaging in acute HIV.

Neuroimage Clin. 2018;20:327-335

Authors: Samboju V, Philippi CL, Chan P, Cobigo Y, Fletcher JLK, Robb M, Hellmuth J, Benjapornpong K, Dumrongpisutikul N, Pothisri M, Paul R, Ananworanich J, Spudich S, Valcour V, SEARCH 010/RV254, RV304 protocol teams

Abstract
Background: HIV RNA is identified in cerebrospinal fluid (CSF) within eight days of estimated viral exposure. Neurological findings and impaired neuropsychological testing performance are documented in a subset of individuals with acute HIV infection (AHI). The purpose of this study was to determine whether microstructural white matter and resting-state functional connectivity (rsFC) are disrupted in AHI.
Methods: We examined 49 AHI (100% male; mean age = 30 ± SD 9.9) and 23 HIV-uninfected Thai participants (78% male; age = 30 ± 5.5) with diffusion tensor imaging (DTI) and rsFC acquired at 3 Tesla, and four neuropsychological tests (summarized as NPZ-4). MRI for the AHI group was performed prior to combination antiretroviral treatment (ART) in 26 participants and on average two days (range:1-5) after ART in 23 participants. Fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) were quantified for DTI. Seed-based voxelwise rsFC analyses were completed for the default mode (DMN), fronto-parietal, and salience and 6 subcortical networks. rsFC and DTI analyses were corrected for family-wise error, with voxelwise comparisons completed using t-tests. Group-specific voxelwise regressions were conducted to examine relationships between imaging indices, HIV disease variables, and treatment status.
Results: The AHI group had a mean (SD) CD4 count of 421(234) cells/mm3 plasma HIV RNA of 6.07(1.1) log10 copies/mL and estimated duration of infection of 20(5.5) days. Differences between AHI and CO groups did not meet statistical significance for DTI metrics. Within the AHI group, voxelwise analyses revealed associations between brief exposure to ART and higher FA and lower RD and MD bilaterally in the corpus callosum, corona radiata, and superior longitudinal fasciculus (p < 0.05). Diffusion indices were unrelated to clinical variables or NPZ-4. The AHI group had reduced rsFC between left parahippocampal cortex (PHC) of the DMN and left middle frontal gyrus compared to CO (p < 0.002). Within AHI, ART status was unrelated to rsFC. However, higher CD4 cell count associated with increased rsFC for the right lateral parietal and PHC seeds in the DMN. Direct associations were noted between NPZ-4 correspond to higher rsFC of the bilateral caudate seed (p < 0.002).
Conclusions: Study findings reveal minimal disruption to structural and functional brain integrity in the earliest stages of HIV. Longitudinal studies are needed to determine if treatment with ART initiated in AHI is sufficient to prevent the evolution of brain dysfunction identified in chronically infected individuals.

PMID: 30101063 [PubMed - indexed for MEDLINE]

Aerobic training modulates salience network and default mode network metabolism in subjects with mild cognitive impairment.

Tue, 01/22/2019 - 17:00
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Aerobic training modulates salience network and default mode network metabolism in subjects with mild cognitive impairment.

Neuroimage Clin. 2018;19:616-624

Authors: Porto FHG, Coutinho AM, de Souza Duran FL, de Sá Pinto AL, Gualano B, Buchpiguel CA, Busatto G, Nitrini R, Brucki SMD

Abstract
Aerobic training (AT) is a promising intervention to improve cognitive functioning. However, its modulatory effects on brain networks are not yet entirely understood. Sixty-five subjects with mild cognitive impairment performed a moderate intensity, 24-week AT program. Differences in resting regional brain glucose metabolism (rBGM) with FDG-PET were assessed before and after AT on a voxel-by-voxel basis. Structural equation modeling was used to create latent variables based on regions with significant rBGM changes and to test a hypothetical model about the inter-relationships between these changes. There were significant rBGM reductions in both anterior temporal lobes (ATL), left inferior frontal gyrus, left anterior cingulate cortex, right hippocampus, left meddle frontal gyrus and bilateral caudate nuclei. In contrast, there was an increase in rBGM in the right precuneus and left inferior frontal gyrus. Latent variables reflecting the salience network and ATL were created, while the precuneus represented the default mode network. In the model, salience network rBGM was decreased after AT. In contrast, rBGM in the default mode network increased as a final outcome. This result suggested improved salience network efficacy and increased control over other brain functional networks. The ATL network decreased its rBGM and connected to the salience network and default mode network with positive and negative correlations, respectively. The model fit values reached statistical significance, demonstrating that this model explained the variance in the measured data. In mild cognitive impairment subjects, AT modulated rBGM in salience network and default mode network nodes. Such changes were in the direction of the normally expected resting-state metabolic patterns of these networks.

PMID: 29984169 [PubMed - indexed for MEDLINE]

Interhemispheric connectivity and hemispheric specialization in schizophrenia patients and their unaffected siblings.

Mon, 01/21/2019 - 22:00
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Interhemispheric connectivity and hemispheric specialization in schizophrenia patients and their unaffected siblings.

Neuroimage Clin. 2019 Jan 07;:101656

Authors: Chang X, Collin G, Mandl RCW, Cahn W, Kahn RS

Abstract
Hemispheric integration and specialization are two prominent organizational principles for macroscopic brain function. Impairments of interhemispheric cooperation have been reported in schizophrenia patients, but whether such abnormalities should be attributed to effects of illness or familial risk remains inconclusive. Moreover, it is unclear how abnormalities in interhemispheric connectivity impact hemispheric specialization. To address these questions, we performed magnetic resonance imaging (MRI) in a large cohort of 253 participants, including 84 schizophrenia patients, 106 of their unaffected siblings and 63 healthy controls. Interhemispheric connectivity and hemispheric specialization were calculated from resting-state functional connectivity, and compared across groups. Results showed that schizophrenia patients exhibit lower interhemispheric connectivity as compared to controls and siblings. In addition, patients showed higher levels of hemispheric specialization as compared to siblings. Level of interhemispheric connectivity and hemispheric specialization correlated with duration of illness in patients. No significant alterations were identified in siblings relative to controls on both measurements. Furthermore, alterations in interhemispheric connectivity correlated with changes in hemispheric specialization in patients relative to controls and siblings. Taken together, these results suggest that lower interhemispheric connectivity and associated abnormalities in hemispheric specialization are features of established illness, rather than an expression of preexistent familial risk for schizophrenia.

PMID: 30660663 [PubMed - as supplied by publisher]

Eating in the absence of hunger in young children is related to brain reward network hyperactivity and reduced functional connectivity in executive control networks.

Sun, 01/20/2019 - 15:00
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Eating in the absence of hunger in young children is related to brain reward network hyperactivity and reduced functional connectivity in executive control networks.

Pediatr Obes. 2019 Jan 18;:e12502

Authors: Shapiro ALB, Johnson SL, Sutton B, Legget KT, Dabelea D, Tregellas JR

Abstract
BACKGROUND: Recent work has implicated disinhibited eating behaviours (DEB) as a potential pathway toward obesity development in children. However, the underlying neurobiology of disinhibited eating behaviours in young, healthy weight children, prior to obesity development, remains unknown.
OBJECTIVES: This study tested the relationship between DEB and intrinsic neuronal activity and connectivity in young children without obesity.
METHODS: Brain networks implicated in overeating including reward, salience and executive control networks, and the default mode network were investigated. DEB was measured by the eating in the absence of hunger (EAH) paradigm with postlunch kilocalories consumed from highly palatable foods (EAH kcal) used as the predictor. Intrinsic neuronal activity within and connectivity between specified networks were measured via resting-state functional magnetic resonance imaging. Eighteen typically developing children (mean age = 5.8 years) were included.
RESULTS: EAH kcal was positively associated with activity of the nucleus accumbens, a major reward network hub (P < 0.05, corrected). EAH kcal was negatively associated with intrinsic prefrontal cortex connectivity to the striatum (P < 0.01, corrected).
CONCLUSIONS: These results suggest that neural aspects of DEB are detectable in young children without obesity, providing a potential tool to better understand the development of obesity in this population.

PMID: 30659756 [PubMed - as supplied by publisher]

Characterization of Autism Spectrum Disorder across the Age Span by Intrinsic Network Patterns.

Sun, 01/20/2019 - 15:00
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Characterization of Autism Spectrum Disorder across the Age Span by Intrinsic Network Patterns.

Brain Topogr. 2019 Jan 18;:

Authors: Morgan BR, Ibrahim GM, Vogan VM, Leung RC, Lee W, Taylor MJ

Abstract
Autism spectrum disorder (ASD) is characterized by abnormal functional organization of brain networks, which may underlie the cognitive and social impairments observed in affected individuals. The present study characterizes unique intrinsic connectivity within- and between- neural networks in children through to adults with ASD, relative to controls. Resting state fMRI data were analyzed in 204 subjects, 102 with ASD and 102 age- and sex-matched controls (ages 7-40 years), acquired on a single scanner. ASD was assessed using the autism diagnostic observation schedule (ADOS). BOLD correlations were calculated between 47 regions of interest, spanning seven resting state brain networks. Partial least squares (PLS) analyses evaluated the association between connectivity patterns and ASD diagnosis as well as ASD severity scores. PLS demonstrated dissociable connectivity patterns in those with ASD, relative to controls. Similar patterns were observed in the whole cohort and in a subgroup analysis of subjects under 18 years of age. Greater inter-network connectivity was seen in ASD with greater intra-network connectivity in controls. In conclusion, stronger inter-network and weaker intra-network resting state-fMRI BOLD correlations characterize ASD and may differentiate control and ASD cohorts. These findings are relevant to understanding ASD as a disruption of network topology.

PMID: 30659389 [PubMed - as supplied by publisher]

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning.

Sun, 01/20/2019 - 15:00
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Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning.

NPJ Schizophr. 2019 Jan 18;5(1):2

Authors: Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, Greenshaw AJ, Dursun SM, Venkatasubramanian G

Abstract
In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as 'Emphasis'; standing for 'Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction') that stacks predictions from several 'single-source' models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

PMID: 30659193 [PubMed]

Differences in the functional connectivity density of the brain between individuals with growth hormone deficiency and idiopathic short stature.

Sat, 01/19/2019 - 14:00
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Differences in the functional connectivity density of the brain between individuals with growth hormone deficiency and idiopathic short stature.

Psychoneuroendocrinology. 2018 Dec 21;103:67-75

Authors: Hu Y, Liu X, Chen X, Chen T, Ye P, Chen L, Fu Y, Xie X, Shan X, Yan Z

Abstract
PURPOSE: The aim of the present study was to investigate the differences in the topological organization of functional brain networks between children with growth hormone deficiency (GHD) and those with idiopathic short stature (ISS).
METHODS: Thirty-one children with GHD and fifty-three children with ISS were recruited based on the results of GH stimulation tests. Resting-state fMRI data were acquired from all children. Whole brain functional connectivity density (FCD) analysis and subsequent seed-based functional connectivity analysis were used to explore the differences in functional brain networks between the children with ISS and GHD. Correlation analyses among the results of clinical laboratory examinations, neuropsychological scales and FCD values of different brain regions were applied.
RESULTS: Compared with the ISS group, the GHD group exhibited significantly decreased FCDs in the left postcentral gyrus, right precentral gyrus and left cerebellar lobules 7b and 6. The subsequent functional connectivity analysis found decreased functional connectivity between lobules 7b and 6 of the left cerebellum as well as the left postcentral gyrus and right precentral gyrus in the GHD group compared to that in the ISS group. In addition, the FCD values of region 6 of the left cerebellum in the GHD group were negatively correlated with the scores on the Symptom Checklist-90 and Eysenck Personality Questionnaire. The FCD value of the left postcentral gyrus in children with ISS positively correlated with IGFBP-3 levels and was approximately correlated with IGF-1 levels.
CONCLUSIONS: These findings highlight the impact of growth hormone deficiency on the brain network that mainly involves the somatosensory, somatic motor and cerebellum networks, which may contribute to the behavioural problems observed in these children.

PMID: 30658340 [PubMed - as supplied by publisher]

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