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Background: The brain's dynamic spontaneous neural activity and dynamic functional connectivity (dFC) are both important in supporting cognition, but how these two types of brain dynamics evolve and co-evolve in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remain unclear. The aim of the present study was to investigate recurrent and concurrent patterns of two types of dynamic brain states correlated with cognitive decline. Methods: The present study analyzed resting-state functional magnetic resonance imaging data from 62 SCD patients, 75 MCI patients, and 70 healthy controls (HCs). We used the sliding-window and clustering method to identify two types of recurrent brain states from both dFC and dynamic regional spontaneous activity, as measured by dynamic fractional amplitude of low-frequency fluctuations (dfALFF). Then, the occurrence frequency of a dFC or dfALFF state and the co-occurrence frequency of a pair of dFC and dfALFF states among all time points are extracted for each participant to describe their dynamics brain patterns. Results: We identified a few recurrent states of dfALFF and dFC and further ascertained the co-occurrent patterns of these two types of dynamic brain states (i.e., dfALFF and dFC states). Importantly, the occurrence frequency of a default-mode network (DMN)-dominated dFC state was significantly different between HCs and SCD patients, and the co-occurrence frequencies of a DMN-dominated dFC state and a DMN-dominated dfALFF state were also significantly different between SCD and MCI patients. These two dynamic features were both significantly positively correlated with Mini-Mental State Examination scores. Conclusion: Our findings revealed novel fMRI-based neural signatures of cognitive decline from recurrent and concurrent patterns of dfALFF and dFC, providing strong evidence supporting SCD as the transition phase between normal aging and MCI. This finding holds potential to differentiate SCD patients from HCs via both dFC and dfALFF as objective neuroimaging biomarkers, which may aid in the early diagnosis and intervention of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Amygdala is an important locus of dysfunction implicated in major depressive disorder(MDD). Aberrant amygdala networks(AN) had been reported in resting-state functional magnetic resonance imaging (rs-fMRI) study. The safety and efficacy of acupuncture treatment for MDD have been verified in previous clinical studies. This study is aimed to investigate whether acupuncture at GV20 could modulate the abnormal AN of patients with the first-episode, drug-naïve MDD by using rs-fMRI combined with functional connectivity (FC) method. Thirty MDD patient underwent 6-min rs-fMRI scans respectively before and after 20-min electro-acupuncture stimulate(EAS) at GV20. Twenty-nine healthy subjects underwent only a 6-min rs-fMRI scan. Based on the amygdala as the seed region, FC method was adopted to examine abnormal AN in patients by comparing with healthy subjects and to evaluate the influence of EAS on intrinsic connectivity within the AN in patients with MDD. Compared to healthy subjects, MDD patients had aberrant intrinsic AN which mainly showed increased FC between amygdala and hippocampus, precuneus, precentral gyrus and angular gyrus, as well as decreased FC between amygdala and orbital frontal cortex(OFC). Moreover, our results indicated that EAS at GV20 induced increased/decreased FC between amygdala and certain regions in MDD patients. In addition, the intrinsic amygdala FC within other certain brain regions in MDD patients were regulated by EAS at GV20. The abnormal AN of MDD patients could be modulated by EAS at GV20. Our findings may further provide the potential imaging evidence to support the modulatory mechanisms of acupuncture on MDD. [ABSTRACT FROM AUTHOR]

Background: Recently, subjective cognitive decline (SCD) has been described as the earliest at-risk state of Alzheimer's disease (AD), and drawn attention of investigators. Studies suggested that SCD-community individuals may constitute a more vulnerable population than SCD-clinic patients, therefore, to investigate the early changes of the brain may provide guidance for treatment of the disease. We sought to investigate the changes of structure and functional connectivity alternation of the hippocampus in individuals with SCD recruited from the community using structural and resting-state functional MRI (fMRI). Methods: Thirty-five SCD patients and 32 healthy controls were recruited. Resting-state fMRI data and high-resolution T1-weighted images were collected. Whole-brain voxel-based morphometry was used to examine the brain structural changes. We also used the hippocampal tail and the whole hippocampus as seeds to investigate functional connectivity alternation in SCD. Results: Individuals with SCD showed significant gray matter volume decreases in the bilateral hippocampal tails and enlargement of the bilateral paracentral lobules. We also found that individuals with SCD showed decreased hippocampal tail resting-state functional connectivity (rsFC) with the right medial prefrontal cortex (mPFC) and the left temporoparietal junction (TPJ), and decreased whole hippocampus rsFC with the bilateral mPFC and TPJ. These brain region and FC showing significant differences also showed significantly correlation with Montreal Cognitive Assessment (MoCA) scores. Conclusion: Individuals with SCD recruited from the community is associated with structural and functional changes of the hippocampus, and these changes may serve as potential biomarkers of SCD. Clinical Trial Registration: The Declaration of Helsinki, and the study was registered in http://www.chictr.org.cn. The Clinical Trial Registration Number was ChiCTR-IPR-16009144. [ABSTRACT FROM AUTHOR]

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Although the precise pathophysiology is not fully understood, it is increasingly believed that the central nervous system plays a vital role in the development of PMS. The aim of this study is to elucidate specific functional connectivity between the thalamus and cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 20 PMS patients and 21 healthy controls (HCs). Seed-based functional connectivity between the thalamus and six cortical regions of interest, including the prefrontal cortex (PFC), posterior parietal cortex, somatosensory cortex, motor cortex/supplementary motor area, temporal and occipital lobe, was adopted to identify specific thalamocortical connectivity in the two groups. Correlation analysis was then used to examine relationships between the neuroimaging findings and clinical symptoms. Activity in distinct cortical regions correlated with specific sub-regions of the thalamus in the two groups. Comparison between groups exhibited decreased prefrontal-thalamic connectivity and increased posterior parietal-thalamic connectivity in the PMS patients. Within the PMS group, the daily record of severity of problems (DRSP) score negatively correlated with the prefrontal-thalamic connectivity. Our findings may provide preliminary evidence for abnormal thalamocortical connectivity in PMS patients and may contribute to a better understanding of the pathophysiology of PMS. [ABSTRACT FROM AUTHOR]

Primary dysmenorrhea (PDM), a common gynecological disorder, is associated with structural and functional alterations in several subregions of the anterior cingulate cortex (ACC). However, systematic functional connectivity of the ACC subregions in PDM has not been clarified. In this study, we used resting-state functional magnetic resonance imaging (fMRI) data from forty-eight PDM patients and thirty-eight matched female healthy controls to investigate the functional connectivity of ACC subregions in PDM. Compared to healthy controls, PDM patients exhibited increased connectivity between the caudal ACC (cACC) and primary somatosensory cortex (SI), between the perigenual ACC (pACC) and caudate, and between the subgenual ACC (sACC) and medial prefrontal cortex (mPFC). PDM patients also showed decreased connectivity between the pACC and precuneus. In PDM group, the connectivity of the right pACC-right caudate positively correlated with disease duration, and the connectivity of the left pACC-left precuneus negatively correlated with disease severity. These present findings reveal that abnormal ACC connectivity may be implicated in the PDM-related disturbances in pain sensory, modulation, and affection. We hope that our study could enhance the understanding of the pathophysiology underlying PDM. [ABSTRACT FROM AUTHOR]

Purpose: To investigate differences in hippocampal activity between premenstrual syndrome (PMS) patients and healthy controls, to elucidate the neural mechanisms of PMS.Materials and Methods: Twenty female patients with PMS (PMS group) and 21 healthy controls (HC group) underwent a single-shot gradient-recalled echo planar imaging (EPI) sequence scan during the luteal phase in 3.0 Tesla MRI. Spontaneous neural activity in hippocampus (HIPP) was measured by fractional amplitude of low-frequency fluctuation (fALFF). Functional connectivity (FC) was used to examine the neural networks of PMS patients by selecting the abnormal HIPP as the seed region. All participants completed a daily record of severity of problems (DRSP) questionnaire to measure the severity of clinical symptoms.Results: Results from a two-sample t-test showed increased left HIPP fALFF in the PMS group compared with the HC group (P = 0.042), while there was no between-group difference of fALFF in the right HIPP (P = 0.1011). A secondary analysis using a two-sample t-test with the left HIPP as the seed region, the results revealed that the PMS group exhibited increased FC between the left HIPP and left medial prefrontal cortex (mPFC), left posterior cingulate cortex (PCC), right middle cingulate cortex (MCC), and bilateral precentral cortex (PC), while decreased FC between the left HIPP and right orbitofrontal cortex (OFC). Moreover, the PMS group exhibited higher DRSP scores, which were positively correlated (r = 0.64, P = 0.003) with FC between the left HIPP and mPFC during the luteal phase.Conclusion: Altered spontaneous neural activity and connectivity of left HIPP may be involved in PMS.Level Of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:545-553. [ABSTRACT FROM AUTHOR]

[Display omitted] • The abnormal local and long-range FCD in MCI compared with HCs. • The reversed FCD was found in MCI after treatments of acupoint-related moxibustion. • After moxibustion treatment, the MoCA scores of MCI were improved. Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion. Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively. Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group. These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI. [ABSTRACT FROM AUTHOR]

• An new multi-task learning framework is devised for differential diagnosis of subjective cognitive decline and mild cognive impairment. • The proposed multi-task learning algorithm combines functional and structural brain information. • The proposed method can discover the most disease-related brain regions and their connectivity. • The extensive experiments demonstrate good classification performance against competing techniques. Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique. [Display omitted] [ABSTRACT FROM AUTHOR]