Zang YF papers

Default mode network mediates low-frequency fluctuations in brain activity and behavior during sustained attention

Fri, 07/29/2022 - 18:00

Hum Brain Mapp. 2022 Jul 29. doi: 10.1002/hbm.26024. Online ahead of print.

ABSTRACT

The low-frequency (<0.1 Hz) fluctuation in sustained attention attracts enormous interest in cognitive neuroscience and clinical research since it always leads to cognitive and behavioral lapses. What is the source of the spontaneous fluctuation in sustained attention in neural activity, and how does the neural fluctuation relate to behavioral fluctuation? Here, we address these questions by collecting and analyzing two independent fMRI and behavior datasets. We show that the neural (fMRI) fluctuation in a key brain network, the default-mode network (DMN), mediate behavioral (reaction time) fluctuation during sustained attention. DMN shows the increased amplitude of fluctuation, which correlates with the behavioral fluctuation in a similar frequency range (0.01-0.1 Hz) but not in the lower (<0.01 Hz) or higher (>0.1 Hz) frequency range. This was observed during both auditory and visual sustained attention and was replicable across independent datasets. These results provide a novel insight into the neural source of attention-fluctuation and extend the former concept that DMN was deactivated in cognitive tasks. More generally, our findings highlight the temporal dynamic of the brain-behavior relationship.

PMID:35903957 | DOI:10.1002/hbm.26024

Aberrant visual-related networks in familial cortical myoclonic tremor with epilepsy

Sat, 07/23/2022 - 18:00

Parkinsonism Relat Disord. 2022 Jul 19;101:105-110. doi: 10.1016/j.parkreldis.2022.07.001. Online ahead of print.

ABSTRACT

INTRODUCTION: In familial cortical myoclonic tremor with epilepsy, photic stimulation can trigger visual symptoms and induce a photoparoxysmal response, or photosensitivity, on electroencephalography. However, the mechanism is poorly understood. In this study, we aimed to explore the neuroimaging changes related to visual symptoms and photosensitivity in genetically confirmed familial cortical myoclonic tremor with epilepsy type 1.

METHODS: Resting-state functional magnetic resonance imaging and electroencephalography data were collected from 31 patients carrying the heterozygous pathogenic intronic pentanucleotide (TTTCA)n insertion in the sterile alpha motif domain-containing 12 gene and from 52 age- and sex-matched healthy controls.

RESULTS: (1) Both regional homogeneity and degree centrality values in the bilateral calcarine sulcus were significantly increased in patients compared with healthy controls. (2) When the calcarine sulcus area with increased regional homogeneity was taken as a seed, increased functional connectivity values were observed in the right precentral gyrus, while decreased functional connectivity values were observed in the right superior frontal gyrus and right inferior parietal lobule. (3) Independent component analysis showed increased connectivity in the left calcarine sulcus inside the medial visual network. (4) Correlation analysis revealed a significant positive correlation between regional homogeneity values and frequency of seizure, and photoparoxysmal response grades were positively correlated with the severity of cortical tremor and duration of epilepsy.

CONCLUSION: These findings provide strong evidence for the interpretation of visual symptoms and photosensitivity in familial cortical myoclonic tremor with epilepsy. We speculate that functional changes in the primary visual cortex may be an imaging biomarker for the disease.

PMID:35870251 | DOI:10.1016/j.parkreldis.2022.07.001

Lifespan associations of resting-state brain functional networks with ADHD symptoms

Thu, 07/14/2022 - 18:00

iScience. 2022 Jun 26;25(7):104673. doi: 10.1016/j.isci.2022.104673. eCollection 2022 Jul 15.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

PMID:35832890 | PMC:PMC9272385 | DOI:10.1016/j.isci.2022.104673

Editorial: Investigating the Mechanism of TMS Using Brain Imaging Methods

Tue, 06/07/2022 - 18:00

Front Neurosci. 2022 May 20;16:936219. doi: 10.3389/fnins.2022.936219. eCollection 2022.

NO ABSTRACT

PMID:35669494 | PMC:PMC9163988 | DOI:10.3389/fnins.2022.936219

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder

Mon, 06/06/2022 - 18:00

Transl Psychiatry. 2022 Jun 6;12(1):236. doi: 10.1038/s41398-022-01995-x.

ABSTRACT

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

PMID:35668086 | DOI:10.1038/s41398-022-01995-x

The Location Reliability of the Resting-State fMRI FC of Emotional Regions Towards rTMS Therapy

Tue, 05/24/2022 - 18:00

Neuroinformatics. 2022 May 24. doi: 10.1007/s12021-022-09585-4. Online ahead of print.

ABSTRACT

Resting-state magnetic resonance imaging (RS-fMRI) studies indicated that the repetitive transcranial magnetic stimulation (rTMS) exerts antidepression effect through the functional connectivity (FC) of the DLPFC with the subgenual anterior cingulate cortex (sgACC), pregneual ACC (pgACC), or nucleus accumbens (NAc). It is proposed that the FC-guided individualized precise stimulation on the DLPFC would be more effective. The current study systematically investigated the reliability of the RS-fMRI FC location as well as the FC strength with multiple potential factors. We aimed to provide a stable stimulation target for future FC-guided TMS therapy for affective related disorders. Twenty-one subjects under RS-fMRI conditions with the first two times (V1, V2) scanned on a GE 3 T scanner and the third visit (V3) on a Siemens 3 T scanner. Then the FC strength and location reliability were assessed by using intra-class correlation (ICC) and intra-individual distance, respectively. The factors included deep seed ROIs (midline (mid-) sgACC, left pgACC, mid-pgACC, and left NAc), eyes closed (EC) vs eyes open (EO), frequency bands, FC algorithm (Pearson vs Spearman), scanning length (half a session vs whole session), and location method (FC peak vs center of gravity (COG)). The reliability of the voxel-wise FC strength was low to moderate. The intra-individual distances of the COG were 3.8-7.3 mm across all factors, much smaller than that of FC peak (approximately 30 mm). The COG of seed-based FC might be a potential rTMS stimulation target. Anyway, all potential stimulation targets should be tested in future rTMS treatment studies.

PMID:35608748 | DOI:10.1007/s12021-022-09585-4

Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series

Mon, 05/16/2022 - 18:00

Front Comput Neurosci. 2022 Apr 27;16:822237. doi: 10.3389/fncom.2022.822237. eCollection 2022.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003-0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.

PMID:35573265 | PMC:PMC9094401 | DOI:10.3389/fncom.2022.822237

Toward coordinate-based cognition dictionaries: A brainmap and neurosynth demo

Sun, 05/15/2022 - 18:00

Neuroscience. 2022 May 12:S0306-4522(22)00077-X. doi: 10.1016/j.neuroscience.2022.02.016. Online ahead of print.

ABSTRACT

Characterizing the functional involvement of specific brain regions has long been a central challenge in cognitive neuroscience. Functional magnetic resonance imaging (fMRI) techniques have offered solutions for mapping functional neural networks. The complex nature of brain-function correspondence makes an elaborate task design difficult to fully capture higher-order cognitive function. Other research practices, such as brain-behavior association or between-group comparisons, are thus widely used to explore cognitive correlations with specific brain regions. However, interpreting the results derived from a specific brain region with their underlying cognitive functions has been too general in publications. Here, we use two examples, i.e., a brain-intelligence correlation study and a depression-control comparison meta-study, to demonstrate use of two neuroimaging online databases, BrainMap and Neurosynth. One key utility of the two databases is the collecting results from massive cognitive task-based fMRI (tb-fMRI) studies, i.e., coordinates in standard brain space. Just like looking up a "coordinate-based cognition dictionary", researchers can receive a plethora of related tb-fMRI activation information characterized by cognitive domains, specific cognitive functions, cognitive task paradigms, and related publications. Surprisingly, we found that only less than 1% of brain-behavior association or between-group comparison studies have utilized this dictionary approach. We encourage the community to further engage with the existing databases for specific and comprehensive interpretation of neuroimaging as well as guidance of future experimental tb-fMRI design.

PMID:35569642 | DOI:10.1016/j.neuroscience.2022.02.016

Editorial: Improving Diagnosis, Treatment, and Prognosis of Neuropsychiatric Disorders by Leveraging Neuroimaging-based Machine Learning

Mon, 05/02/2022 - 18:00

Front Neurosci. 2022 Apr 13;16:891337. doi: 10.3389/fnins.2022.891337. eCollection 2022.

NO ABSTRACT

PMID:35495055 | PMC:PMC9043237 | DOI:10.3389/fnins.2022.891337