Most recent paper

Increased attentional network activity in premature ejaculation patients with anxiety revealed by resting-state fMRI

Fri, 07/30/2021 - 18:00

Eur J Neurosci. 2021 Jul 29. doi: 10.1111/ejn.15402. Online ahead of print.


Psychological account hypothesizes that premature ejaculation (PE) is a learned pattern of rapid ejaculation maintained by anxiety about sexual failure, while neuropsychological accounts hypothesizes that PE is the result of dysfunction of central nervous system regulating ejaculatory. However, the central neural mechanism underlying PE patients with anxiety remains unclear. Resting-state functional magnetic resonance imaging (fMRI) data were collected in 20 PE (diagnoses based on PE Guidelines drafted by the International Society for Sexual Medicine (ISSM)) patients with anxiety and 25 matched healthy controls (HC) from January 2019 to December 2020. The values of fractional amplitude of low-frequency fluctuation (fALFF) were compared between groups. Moreover, the correlations between fALFF and the severity of PE and anxiety of patients were examined. PE patients with anxiety had increased fALFF values in the right inferior frontal gyrus (opercular part) and middle frontal gyrus. In addition, significant positive correlations were found between the scores of PE diagnostic tool (PEDT) and fALFF values of the right inferior frontal gyrus (opercular part), as well as the right middle frontal gyrus. Moreover, fALFF values of the right inferior frontal gyrus (opercular part) and middle frontal gyrus were positively correlated with the scores of self-rating anxiety scale (SAS). Our results suggested that increased attentional network activity might play a critical role in the neural basis of PE patients with anxiety.

PMID:34327757 | DOI:10.1111/ejn.15402

The association between white matter hyperintensities, cognition and regional neural activity in healthy subjects

Fri, 07/30/2021 - 18:00

Eur J Neurosci. 2021 Jul 29. doi: 10.1111/ejn.15403. Online ahead of print.


White matter hyperintensities (WMH) are common findings that can be found in physiological aging. Several studies suggest that the disruption of white matter tracts included in WMH could induce abnormal functioning of the respective linked cortical structures, with consequent repercussion on the cerebral functions, included the cognitive sphere. In this cross-sectional research, we analyzed the effects of the total WMH burden (tWMHb) on resting-state functional magnetic resonance imaging (rs-fMRI) and cognition. Functional and structural MR data, as well as the scores of the trail-making test subtests A (TMT-A) and B (TMT-B) of 75 healthy patients, were extracted from the public available Leipzig Study for Mind-Body-Emotion Interactions dataset. tWMHb was extracted from structural data. Spearman's correlation analyses were made for investigating correlations between WMHb and the scores of the cognitive tests. The fractional amplitude of low-frequency fluctuations (fALFF) method was applied for analyzing the rs-fMRI data, adopting a multiple regression model for studying the effects of tWMHb on brain activity. Three different sub-analyses were conducted using different statistical methods. We observed statistically significant correlations between WMHb and the scores of the cognitive tests. The fALFF analysis revealed that tWMHb is associated with the reduction of regional neural activity of several brain areas (in particular the prefrontal cortex, precuneus, and cerebellar crus I/II). We conclude that our findings clarify better the relationships between WMH and cognitive impairment, evidencing that tWMHb is associated with impairments of the neurocognitive function in healthy subjects by inducing a diffuse reduction of the neural activity.

PMID:34327745 | DOI:10.1111/ejn.15403

Research progress on mechanism and imaging of temporal lobe injury induced by radiotherapy for head and neck cancer

Fri, 07/30/2021 - 18:00

Eur Radiol. 2021 Jul 29. doi: 10.1007/s00330-021-08164-6. Online ahead of print.


Radiotherapy (RT) is an effective treatment for head and neck cancer (HNC). Radiation-induced temporal lobe injury (TLI) is a serious complication of RT. Late symptoms of radiation-induced TLI are irreversible and manifest as memory loss, cognitive impairment, and even temporal lobe necrosis (TLN). It is currently believed that the mechanism of radiation-induced TLI involves microvascular injury, neuron and neural stem cell injury, glial cell damage, inflammation, and the production of free radicals. Significant RT-related structural changes and dose-dependent changes in gray matter (GM) and white matter (WM) volume and morphology were observed through computed tomography (CT) and magnetic resonance imaging (MRI) which were common imaging assessment tools. Diffusion tensor imaging (DTI), dispersion kurtosis imaging (DKI), susceptibility-weighted imaging (SWI), resting-state functional magnetic resonance (rs-fMRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET) can be used for early diagnosis and prognosis evaluation according to functional, molecular, and cellular processes of TLI. Early diagnosis of TLI is helpful to reduce the incidence of TLN and its related complications. This review summarizes the clinical features, mechanisms, and imaging of radiation-induced TLI in HNC patients. KEY POINTS: • Radiation-induced temporal lobe injury (TLI) is a clinical complication and its symptoms mainly include memory impairment, headache, and cognitive impairment. • The mechanisms of TLI include microvascular injury, cell injury, and inflammatory and free radical injury. Significant RT-related structural changes and dose-dependent changes in TL volume and morphology were observed through CT and MRI. • SWI, MRS, DTI, and DKI and other imaging examinations can detect anatomical and functional, molecular, and cellular changes of TLI.

PMID:34327577 | DOI:10.1007/s00330-021-08164-6

Abnormal Degree Centrality in White Matter Hyperintensities: A Resting-State Functional Magnetic Resonance Imaging Study

Fri, 07/30/2021 - 18:00

Front Psychiatry. 2021 Jul 13;12:684553. doi: 10.3389/fpsyt.2021.684553. eCollection 2021.


Background: White matter hyperintensities (WMHs) are a common occurrence with aging and are associated with cognitive impairment. However, the neurobiological mechanisms of WMHs remain poorly understood. Functional magnetic resonance imaging (fMRI) is a prominent tool that helps in non-invasive examinations and is increasingly used to diagnose neuropsychiatric diseases. Degree centrality (DC) is a common and reliable index in fMRI, which counts the number of direct connections for a given voxel in a network and reflects the functional connectivity within brain networks. We explored the underlying mechanism of cognitive impairment in WMHs from the perspective of DC. Methods: A total of 104 patients with WMHs and 37 matched healthy controls (HCs) were enrolled in the current study. All participants underwent individual and overall cognitive function tests and resting-state fMRI (rs-fMRI). WMHs were divided into three groups (39 mild WMHs, 37 moderate WMHs, and 28 severe WMHs) according to their Fazekas scores, and the abnormal DC values in the WMHs and HCs groups were analyzed. Results: There was a significant difference in the right inferior frontal orbital gyrus and left superior parietal gyrus between the WMHs and HCs groups. The functional connectivity between the right inferior frontal orbital gyrus and left inferior temporal gyrus, left superior parietal gyrus, and left parietal inferior gyrus was also different in the WMHs group. Conclusion: The change in DC value may be one of the underlying mechanisms of cognitive impairment in individuals with WMHs, which provides us with a new approach to delaying cognitive impairment in WMHs.

PMID:34326785 | PMC:PMC8315277 | DOI:10.3389/fpsyt.2021.684553

A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting-state fMRI

Thu, 07/29/2021 - 18:00

Hum Brain Mapp. 2021 Jul 29. doi: 10.1002/hbm.25610. Online ahead of print.


Individualized treatment of acute stroke depends on the timely detection of ischemia and potentially salvageable tissue in the brain. Using functional MRI (fMRI), it is possible to characterize cerebral blood flow from blood-oxygen-level-dependent (BOLD) signals without the administration of exogenous contrast agents. In this study, we applied spatial independent component analysis to resting-state fMRI data of 37 stroke patients scanned within 24 hr of symptom onset, 17 of whom received follow-up scans the next day. Our analysis revealed "Hypoperfusion spatially-Independent Components" (HICs) whose spatial patterns of BOLD signal resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user-independent manner (area under the curve = 0.93, balanced accuracy = 0.90, sensitivity = 1.00, and specificity = 0.85). Our study therefore presents a new, noninvasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion.

PMID:34323339 | DOI:10.1002/hbm.25610

Resting-State Functional Connectivity of the Ageing Female Brain-Differences Between Young and Elderly Female Adults on Multislice Short TR rs-fMRI

Thu, 07/29/2021 - 18:00

Front Neurol. 2021 Jul 12;12:645974. doi: 10.3389/fneur.2021.645974. eCollection 2021.


Introduction: Age-related brain changes are one of the most important world health problems due to the rising lifespan and size of the elderly populations. The aim of the study was to assess the effect of ageing in women on coordinated brain activity between eight resting-state networks. Material and Methods: The study group comprised 60 healthy female volunteers who were divided into two age groups: younger women (aged 20-30 n = 30) and older women (aged 55-80 n = 30). Resting-state data were collected during a 15 min scan in the eyes-closed condition using a 3T MR scanner. Data were preprocessed and analysed using the CONN toolbox version 19.c. The large-scale network analysis included a priori selected regions of interest of the default mode, the sensorimotor, the visual, the salience, the dorsal attention, the fronto-parietal, the language, and the cerebellar network. Results: Within the visual, the default mode, the salience, and the sensorimotor network, the intra-network resting-state functional connectivity (RSFC) was significantly higher with increasing age. There was also a significant increase in the inter-network RSFC in older females compared to young females found in the following networks: sensorimotor lateral and salience, salience and language, salience and fronto-parietal, cerebellar anterior and default mode, cerebellar posterior and default mode, visual and sensorimotor lateral, visual and sensorimotor, visual lateral and default mode, language and cerebellar anterior, language and cerebellar posterior, fronto-parietal and cerebellar anterior, dorsal attention and sensorimotor, dorsal attention and default mode, sensorimotor superior, and salience. Compared to young females, elderly women presented bilaterally significantly lower inter-network RSFC of the salience supramarginal gyrus and cerebellar posterior, sensorimotor lateral, and cerebellar anterior network, and sensorimotor lateral and cerebellar posterior as well as sensorimotor superior and cerebellar posterior network. Conclusion: Increased RSFC between some brain networks including the visual, the default mode, the salience, the sensorimotor, the language, the fronto-parietal, the dorsal attention, and the cerebellar networks in elderly females may function as a compensation mechanism during the ageing process of the brain. To the best of our knowledge, this study is the first to report the importance of increase of cerebellar networks RSFC during healthy female ageing.

PMID:34322076 | PMC:PMC8311596 | DOI:10.3389/fneur.2021.645974

Selective Functional Network Changes Following tDCS-Augmented Language Treatment in Primary Progressive Aphasia

Thu, 07/29/2021 - 18:00

Front Aging Neurosci. 2021 Jul 12;13:681043. doi: 10.3389/fnagi.2021.681043. eCollection 2021.


OBJECTIVE: Transcranial direct current stimulation (tDCS) has shown promising results when used as an adjunct to behavioral training in neurodegenerative diseases. However, the underlying neural mechanisms are not understood and neuroimaging evidence from pre/post treatment has been sparse. In this study, we examined tDCS-induced neural changes in a language intervention study for primary progressive aphasia (PPA), a neurodegenerative syndrome with language impairment as the primary clinical presentation. Anodal tDCS was applied to the left inferior frontal gyrus (LIFG). To evaluate the hypothesis that tDCS promotes system segregation, analysis focused on understanding tDCS-induced changes in the brain-wide functional network connectivity of the targeted LIFG.

METHODS: Resting-state fMRI data were obtained from 32 participants with PPA before and after receiving a written naming therapy, accompanied either by tDCS or sham stimulation. We focused on evaluating changes in the global connectivity of the stimulated LIFG-triangularis (LIFG-tri) region given its important role in lexical processing. Global connectivity was indexed by the graph-theoretic measure participation coefficient (PC) which quantifies a region's level of system segregation. The values before and after treatment were compared for each condition (tDCS or Sham) as well as with age-matched healthy controls (n = 19).

RESULTS: Higher global connectivity of the LIFG-tri before treatment was associated with greater dementia severity. After treatment, the tDCS group showed a significant decrease in global connectivity whereas the Sham group's did not change, suggesting specific neural effects induced by tDCS. Further examination revealed that the decrease was driven by reduced connectivity between the LIFG-tri and regions outside the perisylvian language area, consistent with the hypothesis that tDCS enhances the segregation of the language system and improves processing efficiency. Additionally, we found that these effects were specific to the LIFG-tri and not observed in other control regions.

CONCLUSION: TDCS-augmented language therapy in PPA increased the functional segregation of the language system, a normalization of the hyper-connectivity observed before treatment. These findings add to our understanding of the nature of tDCS-induced neural changes in disease treatment and have applications for validating treatment efficacy and designing future tDCS and other non-invasive brain stimulation (NIBS) treatments.

PMID:34322010 | PMC:PMC8311858 | DOI:10.3389/fnagi.2021.681043

Age-related functional connectivity along the hippocampal longitudinal axis

Wed, 07/28/2021 - 18:00

Hippocampus. 2021 Jul 28. doi: 10.1002/hipo.23377. Online ahead of print.


Accumulated evidence points toward a long-axis functional division of the hippocampus, with its anterior part primarily associated with emotional processes and the posterior with navigation and cognition. It is yet unclear, however, how functional connectivity between areas along the hippocampal longitudinal axis and other brain regions differ, and how they are affected by age. Applying an anatomically driven general linear model-based functional connectivity analysis on a large database of resting-state fMRI data, we demonstrate that independent of age, the posterior hippocampus is functionally connected primarily with sensory and motor areas, the middle hippocampus with the default mode network, and the anterior with limbic and prefrontal regions. Along with an age-related disintegration of intra-hippocampal BOLD signal uniformity, the middle and posterior sub-regions exhibit mostly decreases in their functional connectivity with cortical regions, whereas the anterior hippocampus and ventral striatum appear to become more synchronized with age. These findings indicate that long-axis hippocampal areas are tuned to particular functional networks, which do not age in a unified manner.

PMID:34319631 | DOI:10.1002/hipo.23377

Convolutional Neural Network with Sparse Strategies to Classify Dynamic Functional Connectivity

Tue, 07/27/2021 - 18:00

IEEE J Biomed Health Inform. 2021 Jul 27;PP. doi: 10.1109/JBHI.2021.3100559. Online ahead of print.


Classification of dynamic functional connectivity (DFC) is becoming a promising approach for diagnosing various neurodegenerative diseases. However, the existing methods generally face the problem of overfitting. To solve it, this paper proposes a convolutional neural network with three sparse strategies named SCNN to classify DFC. Firstly, an element-wise filter is designed to impose sparse constraints on the DFC matrix by replacing the redundant elements with zeroes, where the DFC matrix is specially constructed to quantify the spatial and temporal variation of DFC. Secondly, a 11 convolutional filter is adopted to reduce the dimensionality of the sparse DFC matrix, and remove meaningless features resulted from zero elements in the subsequent convolution process. Finally, an extra sparse optimization classifier is employed to optimize the parameters of the above two filters, which can effectively improve the ability of SCNN to extract discriminative features. Experimental results on multiple resting-state fMRI datasets demonstrate that the proposed model provides a better classification performance of DFC compared with several state-of-the-art methods, and can identify the abnormal brain functional connectivity.

PMID:34314368 | DOI:10.1109/JBHI.2021.3100559

Dysconnectivity of the amygdala and dorsal anterior cingulate cortex in drug-naive post-traumatic stress disorder

Mon, 07/26/2021 - 18:00

Eur Neuropsychopharmacol. 2021 Jul 23;52:84-93. doi: 10.1016/j.euroneuro.2021.06.010. Online ahead of print.


Convergent studies have highlighted the amygdala-based and dorsal anterior cingulate cortex (dACC)-based circuit or network dysfunction in post-traumatic stress disorder (PTSD). However, previous studies are often complicated by various traumatic types, psychiatric comorbidities, chronic illness duration, and medication effect on brain function. Besides, little is known whether the functional integration with amygdala-dACC interaction disrupted or not in PTSD. Here, we investigated effective connectivity (EC) between the amygdala-dACC and rest of the cortex by applying psycho-physiological interaction (PPI) approach to resting-state functional magnetic resonance imaging data of 63 drug-naive PTSD patients and 74 matched trauma-exposed non-PTSD controls. Pearson correlation analysis was performed between EC values extracted from regions with between-group difference and clinical profiles in PTSD patients. We observed distinct amygdala-dACC interaction pattern between PTSD group and the control group, which is composed primarily by positive EC in the former and negative in the latter. In addition, compared with non-PTSD controls, PTSD patients showed increased EC between amygdala-dACC and the prefrontal cortex, left inferior parietal lobule, and bilateral ventral occipital cortex, and decreased EC between amygdala-dACC and the left fusiform gyrus. The EC increase between amygdala-dACC and the right middle frontal gyrus was negatively correlated with the clinician-administered PTSD scale scores in PTSD patients. Aberrent communication between amgydala-dACC and brain regions involved in central executive network and visual systems might be associated with the pathophysiology of PTSD. Further, these findings suggested that dysconnectivity of the amygdala and dACC could be adapted as a relatively early course diagnostic biomarker of PTSD.

PMID:34311210 | DOI:10.1016/j.euroneuro.2021.06.010

Primary visual cortex of the brain is associated with optic nerve head changes in neuromyelitis optica spectrum disorders

Mon, 07/26/2021 - 18:00

Clin Neurol Neurosurg. 2021 Jul 22;208:106822. doi: 10.1016/j.clineuro.2021.106822. Online ahead of print.


OBJECTIVE: To explore the association between the primary visual cortex in the brain and optic nerve head changes, ONH, (structural thickness and microvascular changes) in neuromyelitis optica spectrum disorder (NMOSD).

METHODS: Nineteen patients who were aquaporin-4 (AQP-4) seropositive NMOSD patients and twenty-two healthy controls (HC) were enrolled for this cross-sectional study. Optical coherence tomographic angiography (OCT-A) was used to image and measure the capillaries density (RPC, radial peripapillary capillaries) and structural thickness (pRNFL, peripapillary retinal nerve fiber layer) around the optic nerve head. A resting-state functional magnetic resonance imaging was used to image and evaluate the gray matter volume (GMV) and functional connectivity (FC) the brain of each participant. We assessed the primary visual cortex (lingual gyrus, calcarine sulcus and thalamus) of the brain.

RESULTS: Changes in RPC density showed a significant association (P < 0.05) with FC of the right lingual gyrus, bilateral calcarine gyrus and left thalamus respectively. pRNFL thickness showed significant association with FC of the right lingual gyrus (Rho = 0.374, P = 0.016), right calcarine gyrus (Rho = 0.355, P = 0.023) and left thalamus (Rho = 0.376, P = 0.015) respectively.

CONCLUSIONS: Visual impairment, structural and microvascular changes around optic nerve head is associated with the functional visual networks in NMOSD. Our report suggests that structural and microvascular changes around the ONH reflect the changes in the primary visual cortex of the brain.

PMID:34311202 | DOI:10.1016/j.clineuro.2021.106822

Functional Magnetic Resonance Imaging Signal Variability Is Associated With Neuromodulation in Fibromyalgia

Mon, 07/26/2021 - 18:00

Neuromodulation. 2021 Jul 26. doi: 10.1111/ner.13512. Online ahead of print.


OBJECTIVES: Although primary motor cortex (M1) transcranial direct current stimulation (tDCS) has an analgesic effect in fibromyalgia (FM), its neural mechanism remains elusive. We investigated whether M1-tDCS modulates a regional temporal variability of blood-oxygenation-level-dependent (BOLD) signals, an indicator of the brain's flexibility and efficiency and if this change is associated with pain improvement.

MATERIALS AND METHODS: In a within-subjects cross-over design, 12 female FM patients underwent sham and active tDCS on five consecutive days, respectively. Each session was performed with an anode placed on the left M1 and a cathode on the contralateral supraorbital region. The subjects also participated in resting-state functional magnetic resonance imaging (fMRI) at baseline and after sham and active tDCS. We compared the BOLD signal variability (SDBOLD ), defined as the standard deviation of the BOLD time-series, between the tDCS conditions. Baseline SDBOLD was compared to 15 healthy female controls.

RESULTS: At baseline, FM patients showed reduced SDBOLD in the ventromedial prefrontal cortex (vmPFC), lateral PFC, and anterior insula and increased SDBOLD in the posterior insula compared to healthy controls. After active tDCS, compared to sham, we found an increased SDBOLD in the left rostral anterior cingulate cortex (rACC), lateral PFC, and thalamus. After sham tDCS, compared to baseline, we found a decreased SDBOLD in the dorsomedial PFC and posterior cingulate cortex/precuneus. Interestingly, after active tDCS compared to sham, pain reduction was correlated with an increased SDBOLD in the rACC/vmPFC but with a decreased SDBOLD in the posterior insula.

CONCLUSION: Our findings suggest that M1-tDCS might revert temporal variability of fMRI signals in the rACC/vmPFC and posterior insula linked to FM pain. Changes in neural variability would be part of the mechanisms underlying repetitive M1-tDCS analgesia in FM.

PMID:34309138 | DOI:10.1111/ner.13512

Individual Subject Approaches to Mapping Sensory-Biased and Multiple-Demand Regions in Human Frontal Cortex

Mon, 07/26/2021 - 18:00

Curr Opin Behav Sci. 2021 Aug;40:169-177. doi: 10.1016/j.cobeha.2021.05.002. Epub 2021 Jun 11.


Sensory modality, widely accepted as a key factor in the functional organization of posterior cortical areas, also shapes the organization of human frontal lobes. 'Deep imaging,' or the practice of collecting a sizable amount of data on individual subjects, offers significant advantages in revealing fine-scale aspects of functional organization of the human brain. Here, we review deep imaging approaches to mapping multiple sensory-biased and multiple-demand regions within human lateral frontal cortex. In addition, we discuss how deep imaging methods can be transferred to large public data sets to further extend functional mapping at the group level. We also review how 'connectome fingerprinting' approaches, combined with deep imaging, can be used to localize fine-grained functional organization in individual subjects using resting-state data. Finally, we summarize current 'best practices' for deep imaging.

PMID:34307791 | PMC:PMC8294130 | DOI:10.1016/j.cobeha.2021.05.002

Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

Mon, 07/26/2021 - 18:00

Front Neurol. 2021 Jul 8;12:671894. doi: 10.3389/fneur.2021.671894. eCollection 2021.


Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC-FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

PMID:34305785 | PMC:PMC8297166 | DOI:10.3389/fneur.2021.671894

Hemispheric Difference of Regional Brain Function Exists in Patients With Acute Stroke in Different Cerebral Hemispheres: A Resting-State fMRI Study

Mon, 07/26/2021 - 18:00

Front Aging Neurosci. 2021 Jul 9;13:691518. doi: 10.3389/fnagi.2021.691518. eCollection 2021.


OBJECTIVE: To explore the different compensatory mechanisms of brain function between the patients with brain dysfunction after acute ischemic stroke (AIS) in the dominant hemisphere and the non-dominant hemisphere based on Resting-state Functional Magnetic Resonance Imaging (Rs-fMRI).

METHODS: In this trial, 15 healthy subjects (HS) were used as blank controls. In total, 30 hemiplegic patients with middle cerebral artery acute infarction of different dominant hemispheres were divided into the dominant hemisphere group (DH) and the non-dominant hemisphere group (NDH), scanned by a 3.0 T MRI scanner, to obtain the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) and compare the differences.

RESULTS: Compared with the HS, increased ALFF values in the brain areas, such as the bilateral midbrain, were observed in DH. Meanwhile decreased ReHo values in the brain areas, such as the right postcentral gyrus (BA3), were also observed. Enhanced ALFF values in the brain areas, such as the left BA6, and enhanced ReHo values in the brain areas, such as the left precuneus, were observed in the NDH. The ALFF and ReHo values of the right BA9 and precentral gyrus were both increased. Compared with DH, the NDH group showed lower ALFF values in the left supplementary motor area and lower ReHo values in the right BA10.

CONCLUSION: After acute infarction in the middle cerebral artery of the dominant hemisphere, a compensation mechanism is triggered in brain areas of the ipsilateral cortex regulating motor-related pathways, while some brain areas related to cognition, sensation, and motor in the contralateral cortex are suppressed, and the connection with the peripheral brain regions is weakened. After acute infarction in the middle cerebral artery of the non-dominant hemisphere, compensatory activation appears in motor control-related brain areas of the dominant hemisphere. After acute middle cerebral artery infarction in the dominant hemisphere, compared with the non-dominant hemisphere, functional specificity in the bilateral supplementary motor area weakens. After acute middle cerebral artery infarction in different hemispheres, there are hemispheric differences in the compensatory mechanism of brain function.

PMID:34305571 | PMC:PMC8299339 | DOI:10.3389/fnagi.2021.691518

Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline

Mon, 07/26/2021 - 18:00

Front Aging Neurosci. 2021 Jul 9;13:688113. doi: 10.3389/fnagi.2021.688113. eCollection 2021.


Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer's Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.

PMID:34305568 | PMC:PMC8299728 | DOI:10.3389/fnagi.2021.688113

Representation Learning of Resting State fMRI with Variational Autoencoder

Sun, 07/25/2021 - 18:00

Neuroimage. 2021 Jul 22:118423. doi: 10.1016/j.neuroimage.2021.118423. Online ahead of print.


Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available per subjects. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.

PMID:34303794 | DOI:10.1016/j.neuroimage.2021.118423

Multivariate semi-blind deconvolution of fMRI time series

Sun, 07/25/2021 - 18:00

Neuroimage. 2021 Jul 22:118418. doi: 10.1016/j.neuroimage.2021.118418. Online ahead of print.


Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

PMID:34303793 | DOI:10.1016/j.neuroimage.2021.118418

Pupil-based states of brain integration across cognitive states

Sun, 07/25/2021 - 18:00

Neuroscience. 2021 Jul 22:S0306-4522(21)00370-5. doi: 10.1016/j.neuroscience.2021.07.016. Online ahead of print.


Arousal is a potent mechanism that provides the brain with functional flexibility and adaptability to external conditions. Within the wake state, arousal levels driven by activity in the neuromodulatory systems are related to specific signatures of neural activation and brain synchrony. However, direct evidence is still lacking on the varying effects of arousal on macroscopic brain characteristics and across a variety of cognitive states in humans. Using a concurrent fMRI-pupillometry approach, we used pupil size as a proxy for arousal and obtained patterns of brain integration associated with increasing arousal levels. We carried out this analysis on resting-state data and data from two attentional tasks implicating different cognitive processes. We found that an increasing level of arousal was related to a state of increased brain integration. This effect was prominent in the salience, visual and default-mode networks in all conditions, while other regions showed task-specificity. Increased integration in the salience network was also related to faster pupil dilation in the two attentional tasks. Furthermore, task performance was related to arousal level, with lower accuracy at higher level of arousal. Taken together, our study provides evidence in humans for pupil size as an index of brain network state, and supports the role of arousal as a switch that drives brain coordination in specific brain regions according to the cognitive state.

PMID:34303781 | DOI:10.1016/j.neuroscience.2021.07.016

Widespread plasticity of cognition-related brain networks in single-sided deafness revealed by randomized window-based dynamic functional connectivity

Sat, 07/24/2021 - 18:00

Med Image Anal. 2021 Jul 7;73:102163. doi: 10.1016/ Online ahead of print.


As an extreme type of partial auditory deprivation, single-sided deafness (SSD) has been demonstrated to lead to extensive neural plasticity according to multimodal neuroimaging studies. Among them, resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable information on functional connectivities (FCs). However, most previous SSD rs-fMRI studies assumed that the extracted FC remains stationary during the entire fMRI scan and neglected dynamic functional activities. Existing fixed window-based dynamic FC analysis also ignores dynamic functional activities under different temporal terms. Additionally, due to the cost constraints of using MRI machines, using data-driven methods for unbiased hypothesis investigations may require more effective sample data augmentation techniques. To tackle these challenges and problems together, in this study, we proposed a dynamic window with a random length and position to extract participants' dynamic characteristics under different temporal terms and to extract more information from the dataset. Then, we proposed a nodal efficiency-based correlation matrix to describe the relationships of synergism between regions as features and applied a linear support vector machine (SVM) model to learn the importance of the features, which helped to identify SSD patients and healthy controls. A total of 68 participants (including 23 with left SSD, 20 with right SSD and 25 healthy controls) were enrolled. Our proposed approach with a random window showed clear improvement compared with traditional static and fixed window-based dynamic FC by using the linear SVM model. FCs related to the frontoparietal, somatomotor, dorsal attention, limbic and default mode networks played significant roles in differentiating SSD patients from healthy controls. Additionally, FCs between the somatomotor and frontoparietal networks made the greatest contribution to the classification model. Regarding brain regions, FCs related to the superior frontal gyrus, superior parietal lobule, superior temporal gyrus, amygdala, and orbital gyrus played significant roles. These findings suggest that networks and regions related to higher-order cognitive functions showed the most significant FC alterations in SSD, which may represent a compensatory collaboration of cognitive resources in SSD.

PMID:34303170 | DOI:10.1016/