Most recent paper

Structural anomaly in the reticular formation in narcolepsy type 1, suggesting lower levels of neuromelanin.

Neuroimage Clin. 2019 May 29;23:101875

Authors: Drissi NM, Warntjes M, Wessén A, Szakacs A, Darin N, Hallböök T, Landtblom AM, Gauffin H, Engström M

Abstract
The aim of this study was to investigate structural changes in the brain stem of adolescents with narcolepsy, a disorder characterized by excessive daytime sleepiness, fragmented night-time sleep, and cataplexy. For this purpose, we used quantitative magnetic resonance imaging to obtain R1 and R2 relaxation rates, proton density, and myelin maps in adolescents with narcolepsy (n = 14) and healthy controls (n = 14). We also acquired resting state functional magnetic resonance imaging (fMRI) for brainstem connectivity analysis. We found a significantly lower R2 in the rostral reticular formation near the superior cerebellar peduncle in narcolepsy patients, family wise error corrected p = .010. Narcolepsy patients had a mean R2 value of 1.17 s-1 whereas healthy controls had a mean R2 of 1.31 s-1, which was a large effect size with Cohen d = 4.14. We did not observe any significant differences in R1 relaxation, proton density, or myelin content. The sensitivity of R2 to metal ions in tissue and the transition metal ion chelating property of neuromelanin indicate that the R2 deviant area is one of the neuromelanin containing nuclei of the brain stem. The close proximity and its demonstrated involvement in sleep-maintenance, specifically through orexin projections from the hypothalamus regulating sleep stability, as well as the results from the connectivity analysis, suggest that the observed deviant area could be the locus coeruleus or other neuromelanin containing nuclei in the proximity of the superior cerebellar peduncle. Hypothetically, the R2 differences described in this paper could be due to lower levels of neuromelanin in this area of narcolepsy patients.

PMID: 31174102 [PubMed - as supplied by publisher]

Acute psychosocial stress alters thalamic network centrality.

Neuroimage. 2019 Jun 04;:

Authors: Janis R, Marie U, Karsten M, Lauckner ME, Deniz K, Lina SH, Baczkowski BM, Anahit B, Miray E, Josefin R, Andrea R, Ju BY, Juergen K, Joachim T, Talma H, Arno V, Michael G

Abstract
Acute stress triggers a broad psychophysiological response that is adaptive if rapidly activated and terminated. While the brain controls the stress response, it is strongly affected by it. Previous research of stress effects on brain activation and connectivity has mainly focused on pre-defined brain regions or networks, potentially missing changes in the rest of the brain. We here investigated how both stress reactivity and stress recovery are reflected in whole-brain network topology and how changes in functional connectivity relate to other stress measures. Healthy young males (n = 67) completed the Trier Social Stress Test or a control task. From 60 min before until 105 min after stress onset, blocks of resting-state fMRI were acquired. Subjective, autonomic, and endocrine measures of the stress response were assessed throughout the experiment. Whole-brain network topology was quantified using Eigenvector centrality (EC) mapping, which detects central hubs of a network. Stress influenced subjective affect, autonomic activity, and endocrine measures. EC differences between groups as well as before and after stress exposure were found in the thalamus, due to widespread connectivity changes in the brain. Stress-driven EC increases in the thalamus were significantly correlated with subjective stress ratings and showed non-significant trends for a correlation with heart rate variability and saliva cortisol. Furthermore, increases in thalamic EC and in saliva cortisol persisted until 105 min after stress onset. We conclude that thalamic areas are central for information processing after stress exposure and may provide an interface for the stress response in the rest of the body and in the mind.

PMID: 31173902 [PubMed - as supplied by publisher]

Machine learning in resting-state fMRI analysis.

Magn Reson Imaging. 2019 Jun 04;:

Authors: Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR

Abstract
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

PMID: 31173849 [PubMed - as supplied by publisher]

Intra- and inter-resting-state networks abnormalities in overactive bladder syndrome patients: an independent component analysis of resting-state fMRI.

World J Urol. 2019 Jun 06;:

Authors: Zuo L, Chen J, Wang S, Zhou Y, Wang B, Gu H

Abstract
PURPOSE: This study aims to determine whether intra-network and inter-network brain connectivities are altered using an independent component analysis (ICA).
METHODS: Resting-state functional MRI (rs-fMRI) data were acquired from 26 patients with OAB and 28 healthy controls (HC). Eleven resting-state networks (RSNs) were identified via ICA. General linear model (GLM) was used to compare intra-network FC and inter-network FC of RSNs between the two groups. Pearson correlation analyses were performed to investigate the relationship between the identified RSNs and clinical variables.
RESULTS: Compared with HC, the OAB group showed abnormal FC within the sensorimotor-related network (SMN), the dorsal attention network (DAN), the dorsal visual network (dVN), and the left frontoparietal network (LFPN). With respect to inter-network interactions, decreased FC was detected between the SMN and the anterior default mode network (aDMN).
CONCLUSION: This study demonstrated that abnormal FC between RSNs may reflect the altered resting state of the brain-bladder network. The findings of this study provide complementary evidence that can help further understand the neural substrates of the overactive bladder.

PMID: 31172280 [PubMed - as supplied by publisher]

Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Apr 17;:

Authors: Zöller D, Sandini C, Karahanoğlu FI, Padula MC, Schaer M, Eliez S, Van De Ville D

Abstract
BACKGROUND: Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers.
METHODS: We retrieved and examined dynamic properties of large-scale functional brain networks using innovation-driven coactivation patterns. The study included resting-state functional magnetic resonance scans from 78 patients with 22q11DS and 85 healthy control subjects. After group comparison of temporal brain network activation properties, functional signatures of prodromal psychotic symptoms and anxiety were extracted using multivariate partial least squares correlation.
RESULTS: Patients with 22q11DS had shorter activation in cognitive brain networks, longer activation in emotion processing networks, and generally increased segregation between brain networks. The functional signature of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Further, the functional signature of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral subdivisions of the anterior cingulate and medial prefrontal cortices. Coupling of amygdala with the dorsal anterior cingulate and medial prefrontal cortices was promoting anxiety, whereas coupling with the ventral anterior cingulate and medial prefrontal cortices had a protective function.
CONCLUSIONS: Using innovation-driven coactivation patterns for dynamic large-scale brain network analysis, we uncovered patterns of brain network activation duration and coupling that are relevant in clinical risk factors for psychosis in 22q11DS. Our results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

PMID: 31171499 [PubMed - as supplied by publisher]

Functional Connectivity Between Sensory-Motor Subnetworks Reflects the Duration of Untreated Psychosis and Predicts Treatment Outcome of First-Episode Drug-Naïve Schizophrenia.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Apr 15;:

Authors: Zhang Y, Xu L, Hu Y, Wu J, Li C, Wang J, Yang Z

Abstract
BACKGROUND: Somatic symptoms and motor abnormalities have been consistently reported as typical symptoms of schizophrenia, but evidence linking impaired functional connectivity among the primary sensory-motor network and its associations to schizophrenia is largely lacking. The present study aims to examine abnormal functional connectivity in the sensory-motor network in schizophrenia and its associations with the duration of untreated psychosis and medication treatment effects. We hypothesize that patients with schizophrenia suffer from disrupted functional connectivity between the sensory-motor subnetworks. The degree of impairment in the connectivity could reflect the duration of untreated psychosis and predict outcomes of medication treatment.
METHODS: At baseline, resting-state functional magnetic resonance imaging data were acquired from 60 first-episode patients with drug-naïve schizophrenia (36 were female) and 60 matching normal control subjects (31 were female). After 2 months, 23 patients who received medication treatment and 32 normal control subjects were rescanned. Functional connectivity among subnetworks in the sensory-motor system was compared between the groups and correlated with the duration of untreated psychosis and the treatment outcome.
RESULTS: Patients with schizophrenia showed significantly disrupted functional connectivity in the sensory-motor network. The degree of impairment reflected the duration of untreated psychosis and motor-related symptoms. It further predicted the improvement of positive scores after medication.
CONCLUSIONS: These findings suggest that functional connectivity in the sensory-motor network could indicate the severity of neural impairment in schizophrenia, and it deserves more attention in the search for neuroimaging markers for evaluating neural impairment and prognosis.

PMID: 31171498 [PubMed - as supplied by publisher]

Dynamic thresholding networks for schizophrenia diagnosis.

Artif Intell Med. 2019 May;96:25-32

Authors: Zou H, Yang J

Abstract
BACKGROUND AND OBJECTIVE: Functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) is an effective approach to describe the neural interaction between distributed brain regions. Recent progress in neuroimaging study reported that the connection between regions is time-varying, which may enhance understanding of normal cognition and alterations that result from brain disorders. However, conventional sliding window based dynamic FC (DFC) analysis has several drawbacks, including arbitrary choice of window length, inaccurate descriptor of FC, and the fact that many spurious connections were included in the fully-connected networks due to noise. This study aims to develop an effective dynamic thresholding brain networks method to diagnose schizophrenia.
METHODS: In this study, we proposed a time-varying window length DFC method based on dynamic time warping to construct brain functional networks. To further eliminate the influence of spurious connections caused by noise, orthogonal minimum spanning tree was applied in these networks to generate time-varying window length dynamic thresholding FC (TVWDTFC) networks. To validate the effectiveness of our proposed method, experiments were conducted on a dataset, which including 56 individuals with schizophrenia and 74 healthy controls.
RESULTS: We achieved a classification accuracy of 0.8077 (p < 0.001, permutation test) using support vector machine. Experimental results demonstrated that the proposed method outperforms several state-of-the-art approaches, which verified the effectiveness of our proposed TVWDTFC method in schizophrenia diagnosis. Additionally, we also found that the selected discriminative features were mostly distributed in frontal, parietal, and limbic area.
CONCLUSIONS: The results suggest that our approach may be a promising tool for computer-aided diagnosis of schizophrenia.

PMID: 31164208 [PubMed - in process]

A failed top-down control from the prefrontal cortex to the amygdala in generalized anxiety disorder: evidence from resting-state fMRI with Granger causality analysis.

Neurosci Lett. 2019 Jun 01;:134314

Authors: Dong M, Xia L, Lu M, Li C, Xu K, Zhang L

Abstract
In generalized anxiety disorder (GAD), abnormal top-down control from the prefrontal cortex (PFC) to the amygdala is a widely accepted hypothesis through which an "emotional dysregulation model" may be explained. However, whether and how the PFC directly exerts abnormal top-down control on the amygdala remains largely unknown. We aimed to investigate the amygdala-based effective connectivity by using Granger causality analysis (GCA). Thirty-five drug-naive patients with GAD and thirty-six healthy controls (HC) underwent resting-state functional MR imaging. We used seed-based Granger causality analysis to examine the effective connectivity between the bilateral amygdala and the whole brain. The amygdala-based effective connectivity was compared between the HC and GAD groups. The results showed that, in the HC group, the left middle frontal gyrus exerted an inhibitory influence on the right amygdala, while in the GAD group, this influence was disrupted (single voxel P < 0.001, Gaussian random field corrected with P < 0.01). Our findings support and advance the "insufficient top-down control" hypothesis by identifying a failed top-down control from the prefrontal cortex to the amygdala in GAD.

PMID: 31163226 [PubMed - as supplied by publisher]

Scale-Free Amplitude Modulation of Low-Frequency Fluctuations in Episodic Migraine.

Pain. 2019 May 16;:

Authors: Hodkinson DJ, Lee D, Becerra L, Borsook D

Abstract
Arrhythmic fluctuations in neural activity occur at many levels of the nervous system. Such activity does not have a characteristic temporal periodicity but can exhibit statistical similarities, most commonly power-law scaling behavior, which is indicative of scale-free dynamics. The recurrence of scaling laws across many different systems and its manifestation in behavior has prompted a search for unifying principles in human brain function. With this in mind, a focused search for abnormities in scale-free dynamics is of considerable clinical relevance to migraine and other clinical pain disorders. Here we examined the scale-free properties of the resting-state fMRI signal in the broadband frequency range known to be related to spontaneous neural activity (0.01-0.1Hz). In a large cohort of episodic migraine patients (N=40), we observed that the strength of long-range temporal correlations in the fMRI signal (captured by the scaling exponent α) was significantly higher in the sensorimotor network compared to healthy controls. Increases in the scaling exponent were positively correlated with fMRI signal variance and negatively correlated with patient's self-reported headache intensity. These changes in the fMRI signal suggest that the temporal structure of amplitude fluctuations carries valuable information about the dynamic state of the underlying neuronal networks and ensuing sensory impairments in migraine. The demonstrated scaling laws pose a novel quantitative approach for examining clinically relevant inter-individual variability in migraine and other pain disorders.

PMID: 31162336 [PubMed - as supplied by publisher]

Abnormal Neural Activity in Children With Diffuse Intrinsic Pontine Glioma Had Manifested Deficit in Behavioral Inhibition: A Resting-State Functional MRI Study.

J Comput Assist Tomogr. 2019 May 31;:

Authors: Cheng X, Gao PY

Abstract
PURPOSE: The purpose of this study was to investigate whether alterations of regional neural function in children with diffuse intrinsic pontine glioma (DIPG) had manifested deficit in behavioral inhibition using resting-state functional MRI (rs-fMRI).
METHODS: There were 17 participants with DIPG who took part in the study. Eight children were with deficit in behavioral inhibition, whereas the other 9 children did not obtain deficit in behavioral inhibition. Five healthy children with age, sex, and education matched to the study group also participated as the control group. These 3 groups underwent rs-fMRI, and the results were then converted to amplitude of low-frequency fluctuation (ALFF) data. Amplitude of low-frequency fluctuation data were further analyzed by single-factor analysis of variance comparing among 3 groups based on the whole brain levels. Amplitude of low-frequency fluctuation results were subjected to t test of voxel-wised comparison to derive the rs-fMRI brain function differences between the 2 DIPG groups. The Pearson correlation between ALFF values of abnormal regions found in 3 groups and the scores obtained according to the Child Behavior Checklist were analyzed.
RESULTS: The 3 groups had shown significant differences in terms of the ALFF results, with the ALFF increased in several brain regions (P < 0.05, corrected with AlphaSim, clusters >59 voxels), which include left supramarginal gyrus, left dorsolateral superior frontal gyrus, right precentral gyrus, and right middle frontal gyrus. Participants with deficit in behavioral inhibition had shown significant differences (ALFF decreased) in several brain regions, including left dorsolateral superior frontal gyrus and right fusiform gyrus (P < 0.05, corrected with AlphaSim, clusters >123 voxels), whereas other brain regions had shown ALFF increased, including left supramarginal gyrus, left middle frontal gyrus, and right medial superior frontal gyrus (P < 0.05, corrected with AlphaSim, clusters >123 voxels). There was no significant correlation between ALFF values and Child Behavior Checklist scores (P > 0.05).
CONCLUSIONS: These findings of focal spontaneous hyperfunction and hypofunction, which correlate with deficit in behavioral inhibition processing, and the abnormal brain regions are considered to be inefficient (in regions of the brain that may relate to compensatory brain and behavioral functioning, and it may be that the brain region needs to exert extra energy to perform a task to the same degree as the control group) or inability (inability in a certain region, or underpowered), pointing to a pathophysiologic process in executive dysfunction.

PMID: 31162235 [PubMed - as supplied by publisher]

Early Functional Connectivity Predicts Recovery from Visual Field Defects after Stroke.

J Stroke. 2019 May;21(2):207-216

Authors: Kim YH, Cho AH, Kim D, Kim SM, Lim HT, Kwon SU, Kim JS, Kang DW

Abstract
Background and PURPOSE: We aimed to assess whether early resting-state functional connectivity (RSFC) changes measured via functional magnetic resonance imaging (fMRI) could predict recovery from visual field defect (VFD) in acute stroke patients.
METHODS: Patients with VFD due to acute ischemic stroke in the visual cortex and age-matched healthy controls were prospectively enrolled. Serial resting-state (RS)-fMRI and Humphrey visual field (VF) tests were performed within 1 week and at 1 and 3 months (additional VF test at 6 months) after stroke onset in the patient group. The control group also underwent RS-fMRI and a Humphrey VF test. The changes in RSFCs and VF scores (VFSs) over time and their correlations were investigated.
RESULTS: In 32 patients (65±10 years, 25 men), the VFSs were lower and the interhemispheric RSFC in the visual cortices was decreased compared to the control group (n=15, 62±6 years, seven men). The VFSs and interhemispheric RSFC in the visual cortex increased mainly within the first month after stroke onset. The interhemispheric RSFC and VFSs were positively correlated at 1 month after stroke onset. Moreover, the interhemispheric RSFCs in the visual cortex within 1 week were positively correlated with the follow-up VFSs.
CONCLUSIONS: Interhemispheric RSFCs in the visual cortices within 1 week after stroke onset may be a useful biomarker to predict long-term VFD recovery.

PMID: 31161764 [PubMed]

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Brain Topogr. 2019 Jun 03;:

Authors: Lee YB, Yoo K, Roh JH, Moon WJ, Jeong Y

Abstract
Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

PMID: 31161473 [PubMed - as supplied by publisher]

Treatment-naïve first episode depression classification based on high-order brain functional network.

J Affect Disord. 2019 May 28;256:33-41

Authors: Zheng Y, Chen X, Li D, Liu Y, Tan X, Liang Y, Zhang H, Qiu S, Shen D

Abstract
BACKGROUND: Recent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging.
METHODS: We enrolled 82 treatment-naïve first episode depression (FED) adults and 72 matched normal control (NC). A computer-aided diagnosis framework was utilized to classify the FEDs from the NCs based on the features extracted from not only traditional "low-order" FC networks (LON) based on temporal synchronization of original rs-fMRI signals, but also "high-order" FC networks (HON) that characterize more complex functional interactions via correlation of the dynamic (time-varying) FCs. We contrasted a classifier using HON feature (CHON) and compared its performance with using LON only (CLON). Finally, an integrated classification model with both features was proposed to further enhance FED classification.
RESULTS: The CHON had significantly improved diagnostic accuracy compared to the CLON (82.47% vs. 67.53%). Joint classification further improved the performance (83.77%). The brain regions with potential diagnostic values mainly encompass the high-order cognitive function-related networks. Importantly, we found previously less-reported potential imaging biomarkers that involve the vermis and the crus II in the cerebellum.
LIMITATIONS: We only used one imaging modality and did not examine data from different subtypes of depression.
CONCLUSIONS: Depression classification could be significantly improved by using HON features that better capture the higher-level brain functional interactions. The findings suggest the importance of higher-level cerebro-cerebellar interactions in the pathophysiology of MDD.

PMID: 31158714 [PubMed - as supplied by publisher]

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation.

Neuroimage. 2019 May 31;:

Authors: Afyouni S, Smith SM, Nichols TE

Abstract
The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

PMID: 31158478 [PubMed - as supplied by publisher]

Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis.

Soc Cogn Affect Neurosci. 2019 May 31;14(5):529-538

Authors: Lin SY, Lee CC, Chen YS, Kuo LW

Abstract
Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal-valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.

PMID: 31157395 [PubMed - in process]

Schizophrenia Exhibits Bi-directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits.

Cereb Cortex. 2019 Jun 03;:

Authors: Ji JL, Diehl C, Schleifer C, Tamminga CA, Keshavan MS, Sweeney JA, Clementz BA, Hill SK, Pearlson G, Yang G, Creatura G, Krystal JH, Repovs G, Murray J, Winkler A, Anticevic A

Abstract
Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia (SCZ), yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in SCZ. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in SCZ, calling into question accounts of exclusively focal functional disturbances.

PMID: 31157363 [PubMed - as supplied by publisher]

Disrupted Resting Frontal-Parietal Attention Network Topology Is Associated With a Clinical Measure in Children With Attention-Deficit/Hyperactivity Disorder.

Front Psychiatry. 2019;10:300

Authors: Wang Y, Tao F, Zuo C, Kanji M, Hu M, Wang D

Abstract
Purpose: Although alterations in resting-state functional connectivity between brain regions have been reported in children with attention-deficit/hyperactivity disorder (ADHD), the spatial organization of these changes remains largely unknown. Here, we studied frontal-parietal attention network topology in children with ADHD, and related topology to a clinical measure of disease progression. Methods: Resting-state fMRI scans were obtained from New York University Child Study Center, including 119 children with ADHD (male n = 89; female n = 30) and 69 typically developing controls (male n = 33; female n = 36). We characterized frontal-parietal functional networks using standard graph analysis (clustering coefficient and shortest path length) and the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. Results: Clustering coefficient and path length in the frontal-parietal attention network were similar in children with ADHD and typically developing controls; however, diameter was greater and leaf number, tree hierarchy, and kappa were lower in children with ADHD, and were significantly correlated with ADHD symptom score. There were significant alterations in nodal eccentricity in children with ADHD, involving prefrontal and occipital cortex regions, which are compatible with the results of previous ADHD studies. Conclusions: Our results indicate the tendency to deviate from a more centralized organization (star-like topology) towards a more decentralized organization (line-like topology) in the frontal-parietal attention network of children with ADHD. This represents a more random network that is associated with impaired global efficiency and network decentralization. These changes appear to reflect clinically relevant phenomena and hold promise as markers of disease progression.

PMID: 31156474 [PubMed]

Frequency-Dependent Spatial Distribution of Functional Hubs in the Human Brain and Alterations in Major Depressive Disorder.

Front Hum Neurosci. 2019;13:146

Authors: Ries A, Hollander M, Glim S, Meng C, Sorg C, Wohlschläger A

Abstract
Alterations in large-scale brain intrinsic functional connectivity (FC), i.e., coherence between fluctuations of ongoing activity, have been implicated in major depressive disorder (MDD). Yet, little is known about the frequency-dependent alterations of FC in MDD. We calculated frequency specific degree centrality (DC) - a measure of overall FC of a brain region - within 10 distinct frequency sub-bands accessible from the full range of resting-state fMRI BOLD fluctuations (i.e., 0.01-0.25 Hz) in 24 healthy controls and 24 MDD patients. In healthy controls, results reveal a frequency-specific spatial distribution of highly connected brain regions - i.e., hubs - which play a fundamental role in information integration in the brain. MDD patients exhibited significant deviations from the healthy DC patterns, with decreased overall connectedness of widespread regions, in a frequency-specific manner. Decreased DC in MDD patients was observed predominantly in the occipital cortex at low frequencies (0.01-0.1 Hz), in the middle cingulate cortex, sensorimotor cortex, lateral parietal cortex, and the precuneus at middle frequencies (0.1-0.175 Hz), and in the anterior cingulate cortex at high frequencies (0.175-0.25 Hz). Additionally, decreased DC of distinct parts of the insula was observed across low, middle, and high frequency bands. Frequency-specific alterations in the DC of the temporal, insular, and lateral parietal cortices correlated with symptom severity. Importantly, our results indicate that frequency-resolved analysis within the full range of frequencies accessible from the BOLD signal - also including higher frequencies (>0.1 Hz) - reveals unique information about brain organization and its changes, which can otherwise be overlooked.

PMID: 31156409 [PubMed]

Temporal Variability of Cortical Gyral-Sulcal Resting State Functional Activity Correlates With Fluid Intelligence.

Front Neural Circuits. 2019;13:36

Authors: Yang S, Zhao Z, Cui H, Zhang T, Zhao L, He Z, Liu H, Guo L, Liu T, Becker B, Kendrick KM, Jiang X

Abstract
The human cerebral cortex is highly convoluted as convex gyri and concave sulci. In the past decades, extensive studies have consistently revealed substantial differences between gyri and sulci in terms of genetics, anatomy, morphology, axonal fiber connections, and function. Although interesting findings have been reported to date to elucidate the functional difference between gyri and sulci, the temporal variability of functional activity, which could explain individual differences in learning and higher-order cognitive functions, and as well as differences in gyri and sulci, remains to be explored. The present study explored the temporal variability of cortical gyral-sulcal resting state functional activity and its association with fluid intelligence measures on the Human Connectome Project dataset. We found that the temporal variance of resting state fMRI BOLD signal was significantly larger in gyri than in sulci. We also found that the temporal variability of certain regions including middle frontal cortex, inferior parietal lobe and visual cortex was positively associated with fluid intelligence. Moreover, those regions were predominately located in gyri rather than in sulci. This study reports initial evidence for temporal variability difference of functional activity between gyri and sulci, and its association with fluid intelligence measures, and thus provides novel insights to understand the mechanism and functional relevance of gyri and sulci.

PMID: 31156400 [PubMed - in process]

Unbalanced Occlusion Modifies the Pattern of Brain Activity During Execution of a Finger to Thumb Motor Task.

Front Neurosci. 2019;13:499

Authors: Tramonti Fantozzi MP, Diciotti S, Tessa C, Castagna B, Chiesa D, Barresi M, Ravenna G, Faraguna U, Vignali C, De Cicco V, Manzoni D

Abstract
In order to assess possible influences of occlusion on motor performance, we studied by functional magnetic resonance imaging (fMRI) the changes in the blood oxygenation level dependent (BOLD) signal induced at brain level by a finger to thumb motor task in a population of subjects characterized by an asymmetric activation of jaw muscles during clenching (malocclusion). In these subjects, appropriate occlusal correction by an oral orthotic (bite) reduced the masticatory asymmetry. The finger to thumb task was performed while the subject's dental arches were touching, in two conditions: (a) with the teeth in direct contact (Bite OFF) and (b) with the bite interposed between the arches (Bite ON). Both conditions required only a very slight activation of masticatory muscles. Maps of the BOLD signal recorded during the movement were contrasted with the resting condition (activation maps). Between conditions comparison of the activation maps (Bite OFF/Bite ON) showed that, in Bite OFF, the BOLD signal was significantly higher in the trigeminal sensorimotor region, the premotor cortex, the cerebellum, the inferior temporal and occipital cortex, the calcarine cortex, the precuneus on both sides, as well as in the right posterior cingulate cortex. These data are consistent with the hypothesis that malocclusion makes movement performance more difficult, leading to a stronger activation of (a) sensorimotor areas not dealing with the control of the involved body part, (b) regions planning the motor sequence, and (c) the cerebellum, which is essential in motor coordination. Moreover, the findings of a higher activation of temporo-occipital cortex and precuneus/cingulus, respectively, suggest that, during malocclusion, the movement occurs with an increased visual imagery activity, and requires a stronger attentive effort.

PMID: 31156377 [PubMed]