New resting-state fMRI related studies at PubMed

Subscribe to New resting-state fMRI related studies at PubMed feed New resting-state fMRI related studies at PubMed
NCBI: db=pubmed; Term=resting state fMRI
Updated: 2 months 12 hours ago

Distinctive Interaction Between Cognitive Networks and the Visual Cortex in Early Blind Individuals.

Tue, 02/05/2019 - 13:20

Distinctive Interaction Between Cognitive Networks and the Visual Cortex in Early Blind Individuals.

Cereb Cortex. 2019 Jan 31;:

Authors: Abboud S, Cohen L

Abstract
In early blind individuals, brain activation by a variety of nonperceptual cognitive tasks extends to the visual cortex, while in the sighted it is restricted to supramodal association areas. We hypothesized that such activation results from the integration of different sectors of the visual cortex into typical task-dependent networks. We tested this hypothesis with fMRI in blind and sighted subjects using tasks assessing speech comprehension, incidental long-term memory and both verbal and nonverbal executive control, in addition to collecting resting-state data. All tasks activated the visual cortex in blind relative to sighted subjects, which enabled its segmentation according to task sensitivity. We then assessed the unique brain-scale functional connectivity of the segmented areas during resting state. Language-related seeds were preferentially connected to frontal and temporal language areas; the seed derived from the executive task was connected to the right dorsal frontoparietal executive network; and the memory-related seed was uniquely connected to mesial frontoparietal areas involved in episodic memory retrieval. Thus, using a broad set of language, executive, and memory tasks in the same subjects, combined with resting state connectivity, we demonstrate the selective integration of different patches of the visual cortex into brain-scale networks with distinct localization, lateralization, and functional roles.

PMID: 30715236 [PubMed - as supplied by publisher]

Editorial for the special issue "Resting-state fMRI and cognition" in Brain and Cognition.

Tue, 02/05/2019 - 13:20
Related Articles

Editorial for the special issue "Resting-state fMRI and cognition" in Brain and Cognition.

Brain Cogn. 2019 Jan 31;:

Authors: Lotze M, Langner R

PMID: 30712965 [PubMed - as supplied by publisher]

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

Tue, 02/05/2019 - 13:20
Related Articles

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

Neuroimage. 2019 01 01;184:901-915

Authors: Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Tachibana Y, Hori M, Kamiya K, Chougar L, Stawiarz L, Hillert J, Bannier E, Kerbrat A, Edan G, Labauge P, Callot V, Pelletier J, Audoin B, Rasoanandrianina H, Brisset JC, Valsasina P, Rocca MA, Filippi M, Bakshi R, Tauhid S, Prados F, Yiannakas M, Kearney H, Ciccarelli O, Smith S, Treaba CA, Mainero C, Lefeuvre J, Reich DS, Nair G, Auclair V, McLaren DG, Martin AR, Fehlings MG, Vahdat S, Khatibi A, Doyon J, Shepherd T, Charlson E, Narayanan S, Cohen-Adad J

Abstract
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

PMID: 30300751 [PubMed - indexed for MEDLINE]

Prediction of individualized task activation in sensory modality-selective frontal cortex with 'connectome fingerprinting'.

Tue, 02/05/2019 - 13:20
Related Articles

Prediction of individualized task activation in sensory modality-selective frontal cortex with 'connectome fingerprinting'.

Neuroimage. 2018 12;183:173-185

Authors: Tobyne SM, Somers DC, Brissenden JA, Michalka SW, Noyce AL, Osher DE

Abstract
The human cerebral cortex is estimated to comprise 200-300 distinct functional regions per hemisphere. Identification of the precise anatomical location of an individual's unique set of functional regions is a challenge for neuroscience that has broad scientific and clinical utility. Recent studies have demonstrated the existence of four interleaved regions in lateral frontal cortex (LFC) that are part of broader visual attention and auditory attention networks (Michalka et al., 2015; Noyce et al., 2017; Tobyne et al., 2017). Due to a large degree of inter-subject anatomical variability, identification of these regions depends critically on within-subject analyses. Here, we demonstrate that, for both sexes, an individual's unique pattern of resting-state functional connectivity can accurately identify their specific pattern of visual- and auditory-selective working memory and attention task activation in lateral frontal cortex (LFC) using "connectome fingerprinting." Building on prior techniques (Saygin et al., 2011; Osher et al., 2016; Tavor et al., 2016; Smittenaar et al., 2017; Wang et al., 2017; Parker Jones et al., 2017), we demonstrate here that connectome fingerprint predictions are far more accurate than group-average predictions and match the accuracy of within-subject task-based functional localization, while requiring less data. These findings are robust across brain parcellations and are improved with penalized regression methods. Because resting-state data can be easily and rapidly collected, these results have broad implications for both clinical and research investigations of frontal lobe function. Our findings also provide a set of recommendations for future research.

PMID: 30092348 [PubMed - indexed for MEDLINE]

Quantifying the performance of MEG source reconstruction using resting state data.

Tue, 02/05/2019 - 13:20
Related Articles

Quantifying the performance of MEG source reconstruction using resting state data.

Neuroimage. 2018 11 01;181:453-460

Authors: Little S, Bonaiuto J, Meyer SS, Lopez J, Bestmann S, Barnes G

Abstract
In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

PMID: 30012537 [PubMed - indexed for MEDLINE]

Ventral striatal dysfunction in cocaine dependence - difference mapping for subregional resting state functional connectivity.

Tue, 02/05/2019 - 13:20
Related Articles

Ventral striatal dysfunction in cocaine dependence - difference mapping for subregional resting state functional connectivity.

Transl Psychiatry. 2018 06 18;8(1):119

Authors: Zhang S, Li CR

Abstract
Research of dopaminergic deficits has focused on the ventral striatum (VS) with many studies elucidating altered resting state functional connectivity (rsFC) in individuals with cocaine dependence (CD). The VS comprises functional subregions and delineation of subregional changes in rsFC requires careful consideration of the differences between addicted and healthy populations. In the current study, we parcellated the VS using whole-brain rsFC differences between CD and non-drug-using controls (HC). Voxels with similar rsFC changes formed functional clusters. The results showed that the VS was divided into 3 subclusters, in the area of the dorsal-anterior VS (daVS), dorsal posterior VS (dpVS), and ventral VS (vVS), each in association with different patterns of rsFC. The three subregions shared reduced rsFC with bilateral hippocampal/parahippocampal gyri (HG/PHG) but also showed distinct changes, including reduced vVS rsFC with ventromedial prefrontal cortex (vmPFC) and increased daVS rsFC with visual cortex in CD as compared to HC. Across CD, daVS visual cortical connectivity was positively correlated with amount of prior-month cocaine use and cocaine craving, and vVS vmPFC connectivity was negatively correlated with the extent of depression and anxiety. These findings suggest a distinct pattern of altered VS subregional rsFC in cocaine dependence, and some of the changes have eluded analyses using the whole VS as a seed region. The findings may provide new insight to delineating VS circuit deficits in cocaine dependence and provide an alternative analytical framework to address functional dysconnectivity in other mental illnesses.

PMID: 29915214 [PubMed - indexed for MEDLINE]

Using diffusion MRI to discriminate areas of cortical grey matter.

Tue, 02/05/2019 - 13:20
Related Articles

Using diffusion MRI to discriminate areas of cortical grey matter.

Neuroimage. 2018 11 15;182:456-468

Authors: Ganepola T, Nagy Z, Ghosh A, Papadopoulo T, Alexander DC, Sereno MI

Abstract
Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state correlations). Diffusion MRI (dMRI) has been widely adopted to analyse white matter microstructure, but scarcely used to distinguish grey matter regions because of the reduced anisotropy there. Nevertheless, differences in the texture of the cortical 'fabric' have long been mapped by histologists to distinguish cortical areas. Reliable area-specific contrast in the dMRI signal has previously been demonstrated in selected occipital and sensorimotor areas. We expand upon these findings by testing several diffusion-based feature sets in a series of classification tasks. Using Human Connectome Project (HCP) 3T datasets and a supervised learning approach, we demonstrate that diffusion MRI is sensitive to architectonic differences between a large number of different cortical areas defined in the HCP parcellation. By employing a surface-based cortical imaging pipeline, which defines diffusion features relative to local cortical surface orientation, we show that we can differentiate areas from their neighbours with higher accuracy than when using only fractional anisotropy or mean diffusivity. The results suggest that grey matter diffusion may provide a new, independent source of information for dividing up the cortex.

PMID: 29274501 [PubMed - indexed for MEDLINE]

Altered functional connectivity of the subthalamus and the bed nucleus of the stria terminalis in obsessive-compulsive disorder.

Tue, 02/05/2019 - 13:20
Related Articles

Altered functional connectivity of the subthalamus and the bed nucleus of the stria terminalis in obsessive-compulsive disorder.

Psychol Med. 2018 04;48(6):919-928

Authors: Cano M, Alonso P, Martínez-Zalacaín I, Subirà M, Real E, Segalàs C, Pujol J, Cardoner N, Menchón JM, Soriano-Mas C

Abstract
BACKGROUND: The assessment of inter-regional functional connectivity (FC) has allowed for the description of the putative mechanism of action of treatments such as deep brain stimulation (DBS) of the nucleus accumbens in patients with obsessive-compulsive disorder (OCD). Nevertheless, the possible FC alterations of other clinically-effective DBS targets have not been explored. Here we evaluated the FC patterns of the subthalamic nucleus (STN) and the bed nucleus of the stria terminalis (BNST) in patients with OCD, as well as their association with symptom severity.
METHODS: Eighty-six patients with OCD and 104 healthy participants were recruited. A resting-state image was acquired for each participant and a seed-based analysis focused on our two regions of interest was performed using statistical parametric mapping software (SPM8). Between-group differences in FC patterns were assessed with two-sample t test models, while the association between symptom severity and FC patterns was assessed with multiple regression analyses.
RESULTS: In comparison with controls, patients with OCD showed: (1) increased FC between the left STN and the right pre-motor cortex, (2) decreased FC between the right STN and the lenticular nuclei, and (3) increased FC between the left BNST and the right frontopolar cortex. Multiple regression analyses revealed a negative association between clinical severity and FC between the right STN and lenticular nucleus.
CONCLUSIONS: This study provides a neurobiological framework to understand the mechanism of action of DBS on the STN and the BNST, which seems to involve brain circuits related with motor response inhibition and anxiety control, respectively.

PMID: 28826410 [PubMed - indexed for MEDLINE]

Preferential susceptibility of limbic cortices to microstructural damage in temporal lobe epilepsy: A quantitative T1 mapping study.

Tue, 02/05/2019 - 13:20
Related Articles

Preferential susceptibility of limbic cortices to microstructural damage in temporal lobe epilepsy: A quantitative T1 mapping study.

Neuroimage. 2018 11 15;182:294-303

Authors: Bernhardt BC, Fadaie F, Vos de Wael R, Hong SJ, Liu M, Guiot MC, Rudko DA, Bernasconi A, Bernasconi N

Abstract
The majority of MRI studies in temporal lobe epilepsy (TLE) have utilized morphometry to map widespread cortical alterations. Morphological markers, such as cortical thickness or grey matter density, reflect combinations of biological events largely driven by overall cortical geometry rather than intracortical tissue properties. Because of its sensitivity to intracortical myelin, quantitative measurement of longitudinal relaxation time (qT1) provides and an in vivo proxy for cortical microstructure. Here, we mapped the regional distribution of qT1 in a consecutive cohort of 24 TLE patients and 20 healthy controls. Compared to controls, patients presented with a strictly ipsilateral distribution of qT1 increases in temporopolar, parahippocampal and orbitofrontal cortices. Supervised statistical learning applied to qT1 maps could lateralize the seizure focus in 92% of patients. Intracortical profiling of qT1 along streamlines perpendicular to the cortical mantle revealed marked effects in upper levels that tapered off at the white matter interface. Findings remained robust after correction for cortical thickness and interface blurring, suggesting independence from previously reported morphological anomalies in this disorder. Mapping of qT1 along hippocampal subfield surfaces revealed marked increases in anterior portions of the ipsilateral CA1-3 and DG that were also robust against correction for atrophy. Notably, in operated patients, qualitative histopathological analysis of myelin stains in resected hippocampal specimens confirmed disrupted internal architecture and fiber organization. Both hippocampal and neocortical qT1 anomalies were more severe in patients with early disease onset. Finally, analysis of resting-state connectivity from regions of qT1 increases revealed altered intrinsic functional network embedding in patients, particularly to prefrontal networks. Analysis of qT1 suggests a preferential susceptibility of ipsilateral limbic cortices to microstructural damage, possibly related to disrupted myeloarchitecture. These alterations may reflect atypical neurodevelopment and affect the integrity of fronto-limbic functional networks.

PMID: 28583883 [PubMed - indexed for MEDLINE]

Interactive effect of 5-HTTLPR and BDNF polymorphisms on amygdala intrinsic functional connectivity and anxiety.

Mon, 02/04/2019 - 12:40

Interactive effect of 5-HTTLPR and BDNF polymorphisms on amygdala intrinsic functional connectivity and anxiety.

Psychiatry Res Neuroimaging. 2019 Jan 29;285:1-8

Authors: Loewenstern J, You X, Merchant J, Gordon EM, Stollstorff M, Devaney J, Vaidya CJ

Abstract
The serotonin transporter (5-HTTLPR) and brain-derived neurotrophic factor (BDNF) gene polymorphisms have been associated with risk for affective disorders and functional variability of the amygdala. We examined whether the two genotypes interactively influence intrinsic functional connectivity (FC) of the amygdala and whether FC mediates the genetic association with anxiety. Eighty genotyped healthy adults underwent resting state fMRI and completed the self-reported State-Trait Anxiety Inventory. Interactive genetic association with anxiety was observed such that effects of 5-HTTLPR depended on the BDNF Val66Met polymorphism (rs6265 variant), with higher anxiety scores in short and Met carriers compared to the other allelic groups. Voxel-wise FC with left and right amygdala seeds identified regions that were sensitive to variability in anxiety scores. A significant moderated mediation model demonstrated that the effect of 5-HTTLPR genotype on anxiety, moderated by BDNF Val66Met genotype, was fully mediated by FC between the left amygdala and the right dorsolateral prefrontal cortex, a cognitive control-related region, during a task-free state. FC was highest in carriers of the 5-HTTLPR short allele and BDNF Met allele. These findings establish intrinsic amygdala-prefrontal functional connectivity as a potential intermediate phenotype for anxiety, an important step toward identification of causal pathways for vulnerability to affective disorders.

PMID: 30711709 [PubMed - as supplied by publisher]

Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project.

Mon, 02/04/2019 - 12:40

Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Dec 05;:

Authors: Oldehinkel M, Mennes M, Marquand A, Charman T, Tillmann J, Ecker C, Dell'Acqua F, Brandeis D, Banaschewski T, Baumeister S, Moessnang C, Baron-Cohen S, Holt R, Bölte S, Durston S, Kundu P, Lombardo MV, Spooren W, Loth E, Murphy DGM, Beckmann CF, Buitelaar JK, EU-AIMS LEAP group

Abstract
BACKGROUND: Resting-state functional magnetic resonance imaging-based studies on functional connectivity in autism spectrum disorder (ASD) have generated inconsistent results. Interpretation of findings is further hampered by small samples and a focus on a limited number of networks, with networks underlying sensory processing being largely underexamined. We aimed to comprehensively characterize ASD-related alterations within and between 20 well-characterized resting-state networks using baseline data from the EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project.
METHODS: Resting-state functional magnetic resonance imaging data was available for 265 individuals with ASD (7.5-30.3 years; 73.2% male) and 218 typically developing individuals (6.9-29.8 years; 64.2% male), all with IQ > 70. We compared functional connectivity within 20 networks-obtained using independent component analysis-between the ASD and typically developing groups, and related functional connectivity within these networks to continuous (overall) autism trait severity scores derived from the Social Responsiveness Scale Second Edition across all participants. Furthermore, we investigated case-control differences and autism trait-related alterations in between-network connectivity.
RESULTS: Higher autism traits were associated with increased connectivity within salience, medial motor, and orbitofrontal networks. However, we did not replicate previously reported case-control differences within these networks. The between-network analysis did reveal case-control differences showing on average 1) decreased connectivity of the visual association network with somatosensory, medial, and lateral motor networks, and 2) increased connectivity of the cerebellum with these sensory and motor networks in ASD compared with typically developing subjects.
CONCLUSIONS: We demonstrate ASD-related alterations in within- and between-network connectivity. The between-network alterations broadly affect connectivity between cerebellum, visual, and sensory-motor networks, potentially underlying impairments in multisensory and visual-motor integration frequently observed in ASD.

PMID: 30711508 [PubMed - as supplied by publisher]

Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior.

Mon, 02/04/2019 - 12:40

Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior.

Neuroimage. 2019 Jan 31;:

Authors: Kashyap R, Kong R, Bhattacharjee S, Li J, Zhou J, Yeo T

Abstract
There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

PMID: 30711467 [PubMed - as supplied by publisher]

Intrinsic mesocorticolimbic connectivity is negatively associated with social amotivation in people with schizophrenia.

Mon, 02/04/2019 - 12:40

Intrinsic mesocorticolimbic connectivity is negatively associated with social amotivation in people with schizophrenia.

Schizophr Res. 2019 Jan 31;:

Authors: Xu P, Klaasen NG, Opmeer EM, Pijnenborg GHM, van Tol MJ, Liemburg EJ, Aleman A

Abstract
BACKGROUND: Social amotivation is a core element of the negative symptoms of schizophrenia. However, it is still largely unknown which neural substrates underpin social amotivation in people with schizophrenia, though deficiencies in the mesocorticolimbic dopamine system have been proposed.
METHODS: We examined the association between social amotivation and substantia nigra/ventral tegmental area-seeded intrinsic connectivity in 84 people with schizophrenia using resting state functional magnetic resonance imaging.
RESULTS: Spontaneous fluctuations of midbrain dopaminergic regions were positively associated with striatal and prefrontal fluctuations in people with schizophrenia. Most importantly, social amotivation was negatively associated with functional connectivity between the midbrain's substantia nigra/ventral tegmental area and medial- and lateral prefrontal cortex, the temporoparietal junction, and dorsal and ventral striatum. These associations were observed independently of depressive and positive symptoms.
CONCLUSIONS: Our findings suggest that social amotivation in people with schizophrenia is associated with altered intrinsic connectivity of mesocorticolimbic pathways linked to cognitive control and reward processing. Dysconnectivity of dopaminergic neuronal ensembles that are fundamental to approach behavior and motivation may help explain the lack of initiative social behavior in people with social amotivation.

PMID: 30711314 [PubMed - as supplied by publisher]

Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease.

Sun, 02/03/2019 - 11:20

Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease.

Neuroimage Clin. 2019 Jan 25;22:101680

Authors: Lin SY, Lin CP, Hsieh TJ, Lin CF, Chen SH, Chao YP, Chen YS, Hsu CC, Kuo LW

Abstract
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.

PMID: 30710870 [PubMed - as supplied by publisher]

Computerized cognitive training for Chinese mild cognitive impairment patients: A neuropsychological and fMRI study.

Sat, 02/02/2019 - 22:40

Computerized cognitive training for Chinese mild cognitive impairment patients: A neuropsychological and fMRI study.

Neuroimage Clin. 2019 Jan 26;22:101691

Authors: Li BY, He NY, Qiao Y, Xu HM, Lu YZ, Cui PJ, Ling HW, Yan FH, Tang HD, Chen SD

Abstract
BACKGROUND: Computerized multi-model training has been widely studied for its effect on delaying cognitive decline. In this study, we designed the first Chinese-version computer-based multi-model cognitive training for mild cognitive impairment (MCI) patients. Neuropsychological effects and neural activity changes assessed by functional MRI were both evaluated.
METHOD: MCI patients in the training group were asked to take training 3-4 times per week for 6 months. Neuropsychological and resting-state fMRI assessment were performed at baseline and at 6 months. Patients in both groups were continuously followed up for another 12 months and assessed by neuropsychological tests again.
RESULTS: 78 patients in the training group and 63 patients in the control group accomplished 6-month follow-up. Training group improved 0.23 standard deviation (SD) of mini-mental state examination, while control group had 0.5 SD decline. Addenbrooke's cognitive examination-revised scores in attention (p = 0.002) and memory (p = 0.006), as well as stroop color-word test interference index (p = 0.038) and complex figure test-copy score (p = 0.035) were also in favor of the training effect. Difference between the changes of two groups after training was not statistically significant. The fMRI showed increased regional activity at bilateral temporal poles, insular cortices and hippocampus. However, difference between the changes of two groups after another 12 months was not statistically significant.
CONCLUSIONS: Multi-model cognitive training help MCI patients to gained cognition benefit, especially in memory, attention and executive function. Functional neuroimaging provided consistent neural activation evidence. Nevertheless, after one-year follow up after last training, training effects were not significant. The study provided new evidence of beneficial effect of multi-model cognitive training.

PMID: 30708349 [PubMed - as supplied by publisher]

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

Sat, 02/02/2019 - 22:40

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

Neuroimage Clin. 2019 Jan 17;21:101678

Authors: Dajani DR, Burrows CA, Odriozola P, Baez A, Nebel MB, Mostofsky SH, Uddin LQ

Abstract
BACKGROUND: Current diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function.
METHOD: Five-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level.
RESULTS: No significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences.
CONCLUSIONS: Using clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.

PMID: 30708240 [PubMed - as supplied by publisher]

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks.

Sat, 02/02/2019 - 22:40

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks.

Neuroimage. 2019 Jan 29;:

Authors: Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR

Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

PMID: 30708106 [PubMed - as supplied by publisher]

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

Sat, 02/02/2019 - 22:40

Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury.

Neuroimage. 2019 Jan 29;:

Authors: Pais-Roldán P, Edlow BL, Jiang Y, Stelzer J, Zou M, Yu X

Abstract
Despite the association between brainstem lesions and coma, a mechanistic understanding of coma pathogenesis and recovery is lacking. We developed a coma model in the rat mimicking human brainstem coma, which allowed multimodal analysis of a brainstem tegmentum lesion's effects on behavior, cortical electrophysiology, and global brain functional connectivity. After coma induction, we observed a transient period (∼1h) of unresponsiveness accompanied by cortical burst-suppression. Comatose rats then gradually regained behavioral responsiveness concurrent with emergence of delta/theta-predominant cortical rhythms in primary somatosensory cortex. During the acute stage of coma recovery (∼1-8h), longitudinal resting-state functional MRI revealed an increase in functional connectivity between subcortical arousal nuclei in the thalamus, basal forebrain, and basal ganglia and cortical regions implicated in awareness. This rat coma model provides an experimental platform to systematically study network-based mechanisms of coma pathogenesis and recovery, as well as to test targeted therapies aimed at promoting recovery of consciousness after coma.

PMID: 30708105 [PubMed - as supplied by publisher]

Functional connectivity changes in core resting state networks are associated with cognitive performance in Systemic Lupus Erythematosus.

Sat, 02/02/2019 - 22:40

Functional connectivity changes in core resting state networks are associated with cognitive performance in Systemic Lupus Erythematosus.

J Comp Neurol. 2019 Feb 01;:

Authors: Nystedt J, Mannfolk P, Jönsen A, Nilsson P, Strandberg TO, Sundgren PC

Abstract
To investigate core resting state networks in SLE patients with and without neuropsychiatric symptoms by examining functional connectivity changes correlating with results of cognitive testing. Structural MRI and resting state-fMRI (rs-fMRI) were performed in 61 female SLE patients (mean age: 36.8 years, range 18.2-52.0 years) and 20 healthy controls (HC) (mean age 36.2 years, range 23.3-52.2 years) in conjunction with clinical examination and cognitive testing. Alterations in core resting state networks, not found in our healthy controls sample, correlated with cognitive performance gauged by neuropsychological tests in non-neuropsychiatric SLE (nNP) as well as in neuropsychiatric SLE patients (NP). The observed pattern of increased functional connectivity in core resting state networks correlated with reduced cognitive performance on all cognitive domains tested and with a heavily focus on DM, CE and DM-CE in the NP subgroup. Furthermore, we found that the observed alterations in memory and psychomotor speed correlated with disease duration. In SLE patients both with and without clinically overt neuropsychiatric manifestations, we found changes in the functional connectivity of core resting state networks essential to cognitive functions. These findings may represent a rewiring of functional architecture in response to neuronal damage and could indicate suboptimal compensatory mechanisms at play. This article is protected by copyright. All rights reserved.

PMID: 30707449 [PubMed - as supplied by publisher]

Spatially Overlapping Regions Show Abnormal Thalamo-frontal Circuit and Abnormal Precuneus in Disorders of Consciousness.

Sat, 02/02/2019 - 22:40

Spatially Overlapping Regions Show Abnormal Thalamo-frontal Circuit and Abnormal Precuneus in Disorders of Consciousness.

Brain Topogr. 2019 Feb 01;:

Authors: Wu X, Xie Q, Liu X, Huang H, Ma Q, Wang J, Zhong M, He Y, Niu C, Chen Y, Deng F, Ni X, He Y, Guo Y, Yu R, Huang R

Abstract
Understanding the neural mechanisms of disorders of consciousness (DOC) is essential for estimating the conscious level and diagnosing DOC patients. Although previous studies reported brain functional connectivity (FC) and spontaneous neural activity patterns associated with consciousness, the relationship between them remains unclear. In this study, we identified the abnormal brain regions in DOC patients by performing voxel-wise FC strength (FCS) and fractional amplitude of low-frequency fluctuations (fALFF) analyses on resting-state functional magnetic resonance imaging data of 15 DOC patients and 24 healthy controls. Furthermore, we detected spatial intersections between two measures and estimated the correlations between either the FCS or the fALFF and the subscales of the Coma Recovery Scale-Revised (CRS-R). We found that the right superior frontal gyrus, left thalamus and right precuneus in which the DOC patients had a lower local FCS and fALFF than healthy controls, are coincident with regions of the mesocircuit model. In the right precuneus, the local FCS/fALFF was significantly positively correlated with the oromotor and motor scores/motor score of the CRS-R. Our findings may indicate that the co-occurrent pattern of spontaneous neural activity and functional connectivity in the thalamo-frontal circuit and the precuneus are associated with motor function in DOC patients.

PMID: 30707390 [PubMed - as supplied by publisher]

Pages