New resting-state fMRI related studies at PubMed

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Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks.

Wed, 05/17/2017 - 13:40
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Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks.

Front Neurosci. 2017;11:238

Authors: Sohn WS, Lee TY, Yoo K, Kim M, Yun JY, Hur JW, Yoon YB, Seo SW, Na DL, Jeong Y, Kwon JS

Abstract
Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.

PMID: 28507502 [PubMed - in process]

The neural correlates of emotional lability in children with ASD.

Wed, 05/17/2017 - 13:40
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The neural correlates of emotional lability in children with ASD.

Brain Connect. 2017 May 15;:

Authors: Bennett R, Somandapalli K, Di Martino A, Roy A

Abstract
OBJECTIVE: Autism spectrum disorder (ASD) is exceptionally heterogeneous in both clinical and physiopathological presentations. Clinical variability applies to ASD-specific symptoms as well as frequent comorbid psychopathology such as emotional lability (EL). To date, the physiopathological underpinnings of the co-occurrence of EL and ASD are unknown. As a first step, we examined within-ASD inter-individual variability of EL and its neuronal correlates using resting-state fMRI (R-fMRI).
METHODS: We analyzed R-fMRI data from 58 children diagnosed with ASD (5-12 years) in relation to the Conners' Parent Rating Scale EL index. We performed both an a priori amygdala region-of-interest (ROI) analysis, and a multivariate unbiased whole-brain data-driven approach.
RESULTS: While no significant brain-behavior relationships were identified regarding amygdala intrinsic functional connectivity (iFC), multivariate whole-brain analyses revealed an extended functional circuitry centered on two regions: middle frontal gyrus (MFG) and posterior insula (PI). Follow-up parametric and non-parametric ROI-analyses of these regions revealed relationships between EL and MFG- and PI-iFC with default, salience, and visual networks suggesting that higher-order cognitive and somatosensory processes are critical for emotion regulation in ASD.
CONCLUSIONS: We did not detect evidence of amygdala iFC underpinning EL in ASD. However, exploratory whole-brain analyses identified large-scale networks that have been previously reported abnormal in ASD. Future studies should consider EL as a potential source of neuronal heterogeneity in ASD and focus on multinetwork interactions.

PMID: 28506079 [PubMed - as supplied by publisher]

Fronto-temporal interactions are functionally relevant for semantic control in language processing.

Tue, 05/16/2017 - 13:05

Fronto-temporal interactions are functionally relevant for semantic control in language processing.

PLoS One. 2017;12(5):e0177753

Authors: Wawrzyniak M, Hoffstaedter F, Klingbeil J, Stockert A, Wrede K, Hartwigsen G, Eickhoff SB, Classen J, Saur D

Abstract
Semantic cognition, i.e. processing of meaning is based on semantic representations and their controlled retrieval. Semantic control has been shown to be implemented in a network that consists of left inferior frontal (IFG), and anterior and posterior middle temporal gyri (a/pMTG). We aimed to disrupt semantic control processes with continuous theta burst stimulation (cTBS) over left IFG and pMTG and to study whether behavioral effects are moderated by induced alterations in resting-state functional connectivity. To this end, we applied real cTBS over left IFG and left pMTG as well as sham stimulation on 20 healthy participants in a within-subject design. Stimulation was followed by resting-state functional magnetic resonance imaging and a semantic priming paradigm. Resting-state functional connectivity of regions of interest in left IFG, pMTG and aMTG revealed highly interconnected left-lateralized fronto-temporal networks representing the semantic system. We did not find any significant direct modulation of either task performance or resting-state functional connectivity by effective cTBS. However, after sham cTBS, functional connectivity between IFG and pMTG correlated with task performance under high semantic control demands in the semantic priming paradigm. These findings provide evidence for the functional relevance of interactions between IFG and pMTG for semantic control processes. This interaction was functionally less relevant after cTBS over aIFG which might be interpretable in terms of an indirect disruptive effect of cTBS.

PMID: 28505211 [PubMed - in process]

Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression.

Tue, 05/16/2017 - 13:05

Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression.

Acta Psychiatr Scand. 2017 May 15;:

Authors: Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, Ma X, Wang Y, Yu H, Li X, Meng Y, Palaniyappan L, Li T

Abstract
OBJECTIVE: Depression in bipolar disorder (BipD) requires a therapeutic approach that is from treating unipolar major depressive disorder (UniD), but to date, no reliable methods could separate these two disorders. The aim of this study was to establish the clinical validity and utility of a non-invasive functional MRI-based method to classify BipD from UniD.
METHOD: The degree of connectivity (degree centrality or DC) of every small unit (voxel) with every other unit of the brain was estimated in 22 patients with BipD and 22 age, gender, and depressive severity-matched patients with UniD and 22 healthy controls. Pattern classification analysis was carried out using a support-vector machine (SVM) approach.
RESULTS: Degree centrality pattern from 8-min resting fMRI discriminated BipD from UniD with an accuracy of 86% and diagnostic odds ratio of 9.6. DC was reduced in the left insula and increased in bilateral precuneus in BipD when compared to UniD. In this sample with a high degree of uncertainty (50% prior probability), positive predictive value of the DC test was 79%.
CONCLUSION: Degree centrality maps are potential candidate measures to separate bipolar depression from unipolar depression. Test performance reported here requires further pragmatic evaluation in regular clinical practice.

PMID: 28504840 [PubMed - as supplied by publisher]

Alterations of resting-state fMRI measurements in individuals with cervical dystonia.

Tue, 05/16/2017 - 13:05

Alterations of resting-state fMRI measurements in individuals with cervical dystonia.

Hum Brain Mapp. 2017 May 15;:

Authors: Li Z, Prudente CN, Stilla R, Sathian K, Jinnah HA, Hu X

Abstract
Cervical dystonia (CD) is a neurological disorder with typical symptoms of involuntary and abnormal movements and postures of the head. CD-associated alterations of functional brain networks have not been well characterized. Previous studies of CD using resting-state functional MRI (rfMRI) are limited in two aspects: (i) the analyses were not directly focused on the functional brain network related to head movement and (ii) rfMRI measurements other than functional connectivity (FC) were not investigated. The present study examined alterations of FC in CD by capitalizing on newly identified brain regions supporting isometric head rotation (Prudente et al.: J Neurosci 35 (2015) 9163-9172). In addition to FC, which only reflects inter-regional signal synchronization, local, or intraregional alterations were also examined using rfMRI measurements of the fractional amplitude of low-frequency fluctuations and regional homogeneity (ReHo). Finally, with alterations of different rfMRI measures identified, a support vector machine (SVM) learning algorithm was implemented for group classification. The results revealed both inter- (FC) and intra-regional (ReHo) alterations extensively distributed in both cortical and subcortical structures; and common alterations of these measures were identified bilaterally in the postcentral gyrus as well as in the basal ganglia and thalamus. Of the rfMRI features examined, seven of them (four FC and three ReHo measures) survived the SVM procedure of recursive feature elimination and together provided the highest group classification accuracy of 90.6%. The present findings extend previous studies of rfMRI in CD and offer insight into the underlying pathophysiology of the disorder in relation to network dysfunction and somatosensory disturbances. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

PMID: 28504361 [PubMed - as supplied by publisher]

A novel approach to map induced activation of neuronal networks using chemogenetics and functional neuroimaging in rats: a proof-of-concept study on the mesocorticolimbic system.

Tue, 05/16/2017 - 13:05
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A novel approach to map induced activation of neuronal networks using chemogenetics and functional neuroimaging in rats: a proof-of-concept study on the mesocorticolimbic system.

Neuroimage. 2017 May 11;:

Authors: Roelofs TJM, Verharen JPH, van Tilborg GAF, Boekhoudt L, van der Toorn A, de Jong JW, Luijendijk MCM, Otte WM, Adan RAH, Dijkhuizen RM

Abstract
Linking neural circuit activation at whole-brain level to neuronal activity at cellular level remains one of the major challenges in neuroscience research. We set up a novel functional neuroimaging approach to map global effects of locally induced activation of specific midbrain projection neurons using chemogenetics (Designer Receptors Exclusively Activated by Designer Drugs (DREADD)-technology) combined with pharmacological magnetic resonance imaging (phMRI) in the rat mesocorticolimbic system. Chemogenetic activation of DREADD-targeted mesolimbic or mesocortical pathways, i.e. projections from the ventral tegmental area (VTA) to the nucleus accumbens (NAcc) or medial prefrontal cortex (mPFC), respectively, induced significant blood oxygenation level-dependent (BOLD) responses in areas with DREADD expression, but also in remote defined neural circuitry without DREADD expression. The time-course of brain activation corresponded with the behavioral output measure, i.e. locomotor (hyper)activity, in the mesolimbic pathway-targeted group. Chemogenetic activation specifically increased neuronal activity, whereas functional connectivity assessed with resting state functional MRI (rs-fMRI) remained stable. Positive and negative BOLD responses distinctively reflected simultaneous ventral pallidum activation and substantia nigra pars reticulata deactivation, respectively, demonstrating the concept of mesocorticolimbic network activity with concurrent activation of the direct and indirect pathways following stimulation of specific midbrain projection neurons. The presented methodology provides straightforward and widely applicable opportunities to elucidate relationships between local neuronal activity and global network activity in a controllable manner, which will increase our understanding of the functioning and dysfunctioning of large-scale neuronal networks in health and disease.

PMID: 28502844 [PubMed - as supplied by publisher]

Resting state functional connectivity in primary insomnia, generalized anxiety disorder and controls.

Sun, 05/14/2017 - 12:00
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Resting state functional connectivity in primary insomnia, generalized anxiety disorder and controls.

Psychiatry Res. 2017 May 08;265:26-34

Authors: Pace-Schott EF, Zimmerman JP, Bottary RM, Lee EG, Milad MR, Camprodon JA

Abstract
Sleep abnormalities are extremely common in anxiety disorders and may contribute to their development and persistence. Their shared pathophysiological mechanisms could thus serve as biomarkers or targets for novel therapeutics. Individuals with Primary Insomnia were age- and sex-matched to controls and to persons with Generalized Anxiety Disorder. All underwent fMRI resting-state scans at 3-T. In Primary Insomnia and controls, sleep was recorded for 2 weeks using diaries and actigraphy. All participants completed state-anxiety and neuroticism inventories. Whole-brain connectivity of 6 fear- and extinction-related seeds were compared between the 3 groups using ANOVA. The only significant between-group main effect was seen for connectivity between the left amygdala seed and a bilateral cluster in the rostral anterior cingulate cortex. The latter is believed to exert top-down control over amygdala activity and their interaction may thus constitute an emotion regulatory circuit. This connectivity was significantly greatest in controls while Primary Insomnia was intermediate between that of controls and Generalized Anxiety Disorder. Across Primary Insomnia and control subjects, mean connectivity decreased with poorer sleep. Across all 3 groups, connectivity decreased with greater neuroticism and pre-scan anxiety. Decreased top-down control of the amygdala may increase risk of developing an anxiety disorder with preexisting Primary Insomnia.

PMID: 28500965 [PubMed - as supplied by publisher]

Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples.

Sat, 05/13/2017 - 11:35
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Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples.

J Affect Disord. 2017 Apr 21;218:346-352

Authors: Zhong X, Shi H, Ming Q, Dong D, Zhang X, Zeng LL, Yao S

Abstract
BACKGROUND: there has been a recent increase in the use of connectome-based multivariate pattern analysis (MVPA) of resting-state functional magnetic resonance imaging (fMRI) data aimed at distinguishing patients with major depressive disorder (MDD) from healthy controls (HCs). However, the validity of this method needs to be confirmed in independent samples.
METHOD: we used resting-state fMRI to explore whole-brain functional connectivity (FC) patterns characteristic of MDD and to confirm the effectiveness of MVPA in distinguishing MDD versus HC groups in two independent samples. The first sample set included 29 MDD patients and 33 HCs and second sample set included 46 MDD patients and 57 HCs.
RESULTS: for the first sample, we obtained a correct classification rate of 91.9% with a sensitivity of 89.6% and specificity of 93.9%. For the second sample, we observed a correct classification rate of 86.4% with a sensitivity of 84.8% and specificity of 87.7%. With both samples, we found that the majority of consensus FCs used for MDD identification were located in the salience network, default mode network, the cerebellum, visual cortical areas, and the affective network.
LIMITATION: we did not analyze potential structural differences between the groups.
CONCLUSION: results suggest that whole-brain FC patterns can be used to differentiate depressed patients from HCs and provide evidence for the potential use of connectome-based MVPA as a complementary tool in the clinical diagnosis of MDD.

PMID: 28499208 [PubMed - as supplied by publisher]

Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest.

Sat, 05/13/2017 - 11:35
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Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest.

Neuropsychopharmacology. 2017 May 12;:

Authors: Price RB, Gates K, Kraynak TE, Thase ME, Siegle GJ

Abstract
Depressed patients show abnormalities in brain connectivity at rest, including hyperconnectivity within the Default Mode Network (DMN). However, there is well-known heterogeneity in the clinical presentation of depression that is overlooked when averaging connectivity data. We used data-driven parsing of neural connectivity to reveal subgroups among 80 depressed patients completing resting state fMRI. Directed functional connectivity paths (eg, region A influences region B) within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, a method shown to accurately recover the direction and presence of connectivity paths in individual participants. Individuals were clustered using community detection on neural connectivity estimates. Subgroups were compared on network features and on clinical and biological/demographic characteristics that influence depression prognosis. Two subgroups emerged. Subgroup A, containing 71% of the patients, showed a typical pattern of connectivity across DMN nodes, as previously reported in depressed patients on average. Subgroup B exhibited an atypical connectivity profile lacking DMN connectivity, with increased dorsal anterior cingulate-driven connectivity paths. Subgroup B members had an over-representation of females (87 vs 65% of Subgroup A;χ(2)=3.89, p=0.049), comorbid anxiety diagnoses (42.6 vs 17.5% of Subgroup A;χ(2)=5.34, p=0.02), and highly recurrent depression (63.2 vs 31.8% of Subgroup A;χ(2)=5.38, p=0.020). Neural connectivity-based categorization revealed an atypical pattern of connectivity in a depressed patient subset that would be overlooked in group comparisons of depressed and healthy participants, and tracks with clinically relevant phenotypes including anxious depression and episodic recurrence. Data-driven parsing suggests heterogeneous substrates of depression; ideally future work building on these findings will inform personalized treatment.Neuropsychopharmacology accepted article preview online, 12 May 2017. doi:10.1038/npp.2017.97.

PMID: 28497802 [PubMed - as supplied by publisher]

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.

Sat, 05/13/2017 - 11:35
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Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.

IEEE J Sel Top Signal Process. 2016 Oct;10(7):120-1213

Authors: Rahim M, Thirion B, Comtat C, Varoquaux G, Alzheimer’s Disease Neuroimaging Initiative

Abstract
Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

PMID: 28496560 [PubMed - in process]

Network-targeted cerebellar transcranial magnetic stimulation improves attentional control.

Sat, 05/13/2017 - 11:35
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Network-targeted cerebellar transcranial magnetic stimulation improves attentional control.

Neuroimage. 2017 May 08;:

Authors: Esterman M, Thai M, Okabe H, DeGutis J, Saad E, Laganiere SE, Halko MA

Abstract
Developing non-invasive brain stimulation interventions to improve attentional control is extremely relevant to a variety of neurologic and psychiatric populations, yet few studies have identified reliable biomarkers that can be readily modified to improve attentional control. One potential biomarker of attention is functional connectivity in the core cortical network supporting attention - the dorsal attention network (DAN). We used a network-targeted cerebellar transcranial magnetic stimulation (TMS) procedure, intended to enhance cortical functional connectivity in the DAN. Specifically, in healthy young adults we administered intermittent theta burst TMS (iTBS) to the midline cerebellar node of the DAN and, as a control, the right cerebellar node of the default mode network (DMN). These cerebellar targets were localized using individual resting-state fMRI scans. Participants completed assessments of both sustained (gradual onset continuous performance task, gradCPT) and transient attentional control (attentional blink) immediately before and after stimulation, in two sessions (cerebellar DAN and DMN). Following cerebellar DAN stimulation, participants had significantly fewer attentional lapses (lower commission error rates) on the gradCPT. In contrast, stimulation to the cerebellar DMN did not affect gradCPT performance. Further, in the DAN condition, individuals with worse baseline gradCPT performance showed the greatest enhancement in gradCPT performance. These results suggest that temporarily increasing functional connectivity in the DAN via network-targeted cerebellar stimulation can enhance sustained attention, particularly in those with poor baseline performance. With regard to transient attention, TMS stimulation improved attentional blink performance across both stimulation sites, suggesting increasing functional connectivity in both networks can enhance this aspect of attention. These findings have important implications for intervention applications of TMS and theoretical models of functional connectivity.

PMID: 28495634 [PubMed - as supplied by publisher]

Decisional impulsivity and the associative-limbic subthalamic nucleus in obsessive-compulsive disorder: stimulation and connectivity.

Sat, 05/13/2017 - 11:35
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Decisional impulsivity and the associative-limbic subthalamic nucleus in obsessive-compulsive disorder: stimulation and connectivity.

Brain. 2017 Feb;140(Pt 2):442-456

Authors: Voon V, Droux F, Morris L, Chabardes S, Bougerol T, David O, Krack P, Polosan M

Abstract
Why do we make hasty decisions for short-term gain? Rapid decision-making with limited accumulation of evidence and delay discounting are forms of decisional impulsivity. The subthalamic nucleus is implicated in inhibitory function but its role in decisional impulsivity is less well-understood. Here we assess decisional impulsivity in subjects with obsessive compulsive disorder who have undergone deep brain stimulation of the limbic and associative subthalamic nucleus. We show that stimulation of the subthalamic nucleus is causally implicated in increasing decisional impulsivity with less accumulation of evidence during probabilistic uncertainty and in enhancing delay discounting. Subthalamic stimulation shifts evidence accumulation in subjects with obsessive-compulsive disorder towards a functional less cautious style closer to that of healthy controls emphasizing its adaptive nature. Thus, subjects with obsessive compulsive disorder on subthalamic stimulation may be less likely to check for evidence (e.g. checking that the stove is on) with no difference in subjective confidence (or doubt). In a separate study, we replicate in humans (154 healthy controls) using resting state functional connectivity, tracing studies conducted in non-human primates dissociating limbic, associative and motor frontal hyper-direct connectivity with anterior and posterior subregions of the subthalamic nucleus. We show lateralization of functional connectivity of bilateral ventral striatum to right anterior ventromedial subthalamic nucleus consistent with previous observations of lateralization of emotionally evoked activity to right ventral subthalamic nucleus. We use a multi-echo sequence with independent components analysis, which has been shown to have enhanced signal-to-noise ratio, thus optimizing visualization of small subcortical structures. These findings in healthy controls converge with the effective contacts in obsessive compulsive disorder patients localized within the anterior and ventral subthalamic nucleus. We further show that evidence accumulation is associated with anterior associative-limbic subthalamic nucleus and right dorsolateral prefrontal functional connectivity in healthy controls, a region implicated in decision-making under uncertainty. Together, our findings highlight specificity of the anterior associative-limbic subthalamic nucleus in decisional impulsivity. Given increasing interest in the potential for subthalamic stimulation in psychiatric disorders and the neuropsychiatric symptoms of Parkinson's disease, these findings have clinical implications for behavioural symptoms and cognitive effects as a function of localization of subthalamic stimulation.

PMID: 28040671 [PubMed - indexed for MEDLINE]

Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia.

Sat, 05/13/2017 - 11:35
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Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia.

Brain. 2017 Feb;140(Pt 2):457-471

Authors: Collins JA, Montal V, Hochberg D, Quimby M, Mandelli ML, Makris N, Seeley WW, Gorno-Tempini ML, Dickerson BC

Abstract
A wealth of neuroimaging research has associated semantic variant primary progressive aphasia with distributed cortical atrophy that is most prominent in the left anterior temporal cortex; however, there is little consensus regarding which region within the anterior temporal cortex is most prominently damaged, which may indicate the putative origin of neurodegeneration. In this study, we localized the most prominent and consistent region of atrophy in semantic variant primary progressive aphasia using cortical thickness analysis in two independent patient samples (n = 16 and 28, respectively) relative to age-matched controls (n = 30). Across both samples the point of maximal atrophy was located in the same region of the left temporal pole. This same region was the point of maximal atrophy in 100% of individual patients in both semantic variant primary progressive aphasia samples. Using resting state functional connectivity in healthy young adults (n = 89), we showed that the seed region derived from the semantic variant primary progressive aphasia analysis was strongly connected with a large-scale network that closely resembled the distributed atrophy pattern in semantic variant primary progressive aphasia. In both patient samples, the magnitude of atrophy within a brain region was predicted by that region's strength of functional connectivity to the temporopolar seed region in healthy adults. These findings suggest that cortical atrophy in semantic variant primary progressive aphasia may follow connectional pathways within a large-scale network that converges on the temporal pole.

PMID: 28040670 [PubMed - indexed for MEDLINE]

A human brain network derived from coma-causing brainstem lesions.

Sat, 05/13/2017 - 11:35
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A human brain network derived from coma-causing brainstem lesions.

Neurology. 2016 Dec 06;87(23):2427-2434

Authors: Fischer DB, Boes AD, Demertzi A, Evrard HC, Laureys S, Edlow BL, Liu H, Saper CB, Pascual-Leone A, Fox MD, Geerling JC

Abstract
OBJECTIVE: To characterize a brainstem location specific to coma-causing lesions, and its functional connectivity network.
METHODS: We compared 12 coma-causing brainstem lesions to 24 control brainstem lesions using voxel-based lesion-symptom mapping in a case-control design to identify a site significantly associated with coma. We next used resting-state functional connectivity from a healthy cohort to identify a network of regions functionally connected to this brainstem site. We further investigated the cortical regions of this network by comparing their spatial topography to that of known networks and by evaluating their functional connectivity in patients with disorders of consciousness.
RESULTS: A small region in the rostral dorsolateral pontine tegmentum was significantly associated with coma-causing lesions. In healthy adults, this brainstem site was functionally connected to the ventral anterior insula (AI) and pregenual anterior cingulate cortex (pACC). These cortical areas aligned poorly with previously defined resting-state networks, better matching the distribution of von Economo neurons. Finally, connectivity between the AI and pACC was disrupted in patients with disorders of consciousness, and to a greater degree than other brain networks.
CONCLUSIONS: Injury to a small region in the pontine tegmentum is significantly associated with coma. This brainstem site is functionally connected to 2 cortical regions, the AI and pACC, which become disconnected in disorders of consciousness. This network of brain regions may have a role in the maintenance of human consciousness.

PMID: 27815400 [PubMed - indexed for MEDLINE]

Thalamo-cortical network activity during spontaneous migraine attacks.

Sat, 05/13/2017 - 11:35
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Thalamo-cortical network activity during spontaneous migraine attacks.

Neurology. 2016 Nov 15;87(20):2154-2160

Authors: Coppola G, Di Renzo A, Tinelli E, Di Lorenzo C, Di Lorenzo G, Parisi V, Serrao M, Schoenen J, Pierelli F

Abstract
OBJECTIVE: We used MRI to search for changes in thalamo-cortical networks and thalamic microstructure during spontaneous migraine attacks by studying at the same time structure with diffusion tensor imaging and resting state function in interconnected brain networks with independent component analysis.
METHODS: Thirteen patients with untreated migraine without aura (MI) underwent 3T MRI scans during an attack and were compared to a group of 19 healthy controls (HC). We collected resting state data in 2 selected networks identified using group independent component (IC) analysis. Fractional anisotropy (FA) values of bilateral thalami were calculated in the same participants and correlated with resting state IC z scores.
RESULTS: Functional connectivity between the executive and the dorso-ventral attention networks was reduced in MI compared to HC. In HC, but not in MI, the higher the IC24 z score, encompassing interconnected areas of the dorso-ventral attention system, the lower the bilateral thalamic FA values. In patients, the higher the executive control network z scores, the lower the number of monthly migraine days.
CONCLUSIONS: These results provide evidence for abnormal connectivity between the thalamus and attentional cerebral networks at rest during migraine attacks. This abnormality could subtend the known ictal impairment of cognitive performance and suggests that the latter might worsen with increasing attack frequency.

PMID: 27742813 [PubMed - indexed for MEDLINE]

Posttraumatic stress disorder symptom severity is associated with reduced default mode network connectivity in individuals with elevated genetic risk for psychopathology.

Fri, 05/12/2017 - 11:20

Posttraumatic stress disorder symptom severity is associated with reduced default mode network connectivity in individuals with elevated genetic risk for psychopathology.

Depress Anxiety. 2017 May 11;:

Authors: Miller DR, Logue MW, Wolf EJ, Maniates H, Robinson ME, Hayes JP, Stone A, Schichman S, McGlinchey RE, Milberg WP, Miller MW

Abstract
BACKGROUND: Accumulating evidence suggests that posttraumatic stress disorder (PTSD) is associated with disrupted default mode network (DMN) connectivity, but findings across studies have not been uniform. Individual differences in relevant genes may account for some of the reported variability in the relationship between DMN connectivity and PTSD. In this study, we investigated this possibility using genome-wide association study (GWAS) derived polygenic risk scores (PRSs) for relevant psychiatric traits. We hypothesized that the association between PTSD and DMN connectivity would be moderated by genetic risk for one or more psychiatric traits such that individuals with elevated polygenic risk for psychopathology and severe PTSD would exhibit disrupted DMN connectivity.
METHODS: Participants were 156 white, non-Hispanic veterans of the wars in Iraq and Afghanistan who were genotyped and underwent resting state functional magnetic resonance imaging and clinical assessment. PRSs for neuroticism, anxiety, major depressive disorder, and cross-disorder risk (based on five psychiatric disorders) were calculated using summary statistics from published large-scale consortia-based GWASs.
RESULTS: Cross-disorder polygenic risk influenced the relationship between DMN connectivity and PTSD symptom severity such that individuals at greater genetic risk showed a significant negative association between PTSD symptom severity and connectivity between the posterior cingulate cortex and right middle temporal gyrus. Polygenic risk for neuroticism, anxiety, and major depressive disorder did not influence DMN connectivity directly or through an interaction with PTSD.
CONCLUSIONS: Findings illustrate the potential power of genome-wide PRSs to advance understanding of the relationship between PTSD and DMN connectivity, a putative neural endophenotype of the disorder.

PMID: 28494120 [PubMed - as supplied by publisher]

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs).

Fri, 05/12/2017 - 11:20
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Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs).

Front Neuroinform. 2017;11:28

Authors: Dimitriadis SI, Salis C, Tarnanas I, Linden DE

Abstract
The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or "nodes," and quantifying the strength of the connections between nodes, or "edges," as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the "true" connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.

PMID: 28491032 [PubMed - in process]

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender.

Fri, 05/12/2017 - 11:20
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Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender.

Front Hum Neurosci. 2017;11:189

Authors: Pezoulas VC, Zervakis M, Michelogiannis S, Klados MA

Abstract
During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

PMID: 28491028 [PubMed - in process]

Concordance of the Resting State Networks in Typically Developing, 6-to 7-Year-Old Children and Healthy Adults.

Thu, 05/11/2017 - 23:10
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Concordance of the Resting State Networks in Typically Developing, 6-to 7-Year-Old Children and Healthy Adults.

Front Hum Neurosci. 2017;11:199

Authors: Thornburgh CL, Narayana S, Rezaie R, Bydlinski BN, Tylavsky FA, Papanicolaou AC, Choudhri AF, Völgyi E

Abstract
Though fairly well-studied in adults, less is known about the manifestation of resting state networks (RSN) in children. We examined the validity of RSN derived in an ethnically diverse group of typically developing 6- to 7-year-old children. We hypothesized that the RSNs in young children would be robust and would reliably show significant concordance with previously published RSN in adults. Additionally, we hypothesized that a smaller sample size using this robust technique would be comparable in quality to pediatric RSNs found in a larger cohort study. Furthermore, we posited that compared to the adult RSNs, the primary sensorimotor and the default mode networks (DMNs) in this pediatric group would demonstrate the greatest correspondence, while the executive function networks would exhibit a lesser degree of spatial overlap. Resting state functional magnetic resonance images (rs-fMRI) were acquired in 18 children between 6 and 7 years recruited from an ethnically diverse population in the Mid-South region of the United States. Twenty RSNs were derived using group independent component analysis and their spatial correspondence with previously published adult RSNs was examined. We demonstrate that the rs-fMRI in this group can be deconstructed into the fundamental RSN as all the major RSNs previously described in adults and in a large sample that included older children can be observed in our sample of young children. Further, the primary visual, auditory, and somatosensory networks, as well as the default mode, and frontoparietal networks derived in this group exhibited a greater spatial concordance with those seen in adults. The motor, temporoparietal, executive control, dorsal attention, and cerebellar networks in children had less spatial overlap with the corresponding RSNs in adults. Our findings suggest that several salient RSNs can be mapped reliably in small and diverse pediatric cohort within a narrow age range and the evolution of these RSNs can be studied reliably in such groups during early childhood and adolescence.

PMID: 28487641 [PubMed - in process]

Intranasal insulin enhances brain functional connectivity mediating the relationship between adiposity and subjective feeling of hunger.

Thu, 05/11/2017 - 23:10
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Intranasal insulin enhances brain functional connectivity mediating the relationship between adiposity and subjective feeling of hunger.

Sci Rep. 2017 May 09;7(1):1627

Authors: Kullmann S, Heni M, Veit R, Scheffler K, Machann J, Häring HU, Fritsche A, Preissl H

Abstract
Brain insulin sensitivity is an important link between metabolism and cognitive dysfunction. Intranasal insulin is a promising tool to investigate central insulin action in humans. We evaluated the acute effects of 160 U intranasal insulin on resting-state brain functional connectivity in healthy young adults. Twenty-five lean and twenty-two overweight and obese participants underwent functional magnetic resonance imaging, on two separate days, before and after intranasal insulin or placebo application. Insulin compared to placebo administration resulted in increased functional connectivity between the prefrontal regions of the default-mode network and the hippocampus as well as the hypothalamus. The change in hippocampal functional connectivity significantly correlated with visceral adipose tissue and the change in subjective feeling of hunger after intranasal insulin. Mediation analysis revealed that the intranasal insulin induced hippocampal functional connectivity increase served as a mediator, suppressing the relationship between visceral adipose tissue and hunger. The insulin-induced hypothalamic functional connectivity change showed a significant interaction with peripheral insulin sensitivity. Only participants with high peripheral insulin sensitivity showed a boost in hypothalamic functional connectivity. Hence, brain insulin action may regulate eating behavior and facilitate weight loss by modifying brain functional connectivity within and between cognitive and homeostatic brain regions.

PMID: 28487570 [PubMed - in process]

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