Most recent paper

Serotonin-norepinephrine reuptake inhibitor antidepressant effects on regional connectivity of the thalamus in persistent depressive disorder: evidence from two randomized, double-blind, placebo-controlled clinical trials

Fri, 05/20/2022 - 18:00

Brain Commun. 2022 Apr 15;4(3):fcac100. doi: 10.1093/braincomms/fcac100. eCollection 2022.


Previous neuroimaging studies have shown that serotonin-norepinephrine reuptake inhibitor antidepressants alter functional activity in large expanses of brain regions. However, it is not clear how these regions are systemically organized on a connectome level with specific topological properties, which may be crucial to revealing neural mechanisms underlying serotonin-norepinephrine reuptake inhibitor treatment of persistent depressive disorder. To investigate the effect of serotonin-norepinephrine reuptake inhibitor antidepressants on brain functional connectome reconfiguration in persistent depressive disorder and whether this reconfiguration promotes the improvement of clinical symptoms, we combined resting-state functional magnetic resonance imaging (fMRI) scans acquired in two randomized, double-blind, placebo-controlled trial studies of serotonin-norepinephrine reuptake inhibitor antidepressant treatment of patients with persistent depressive disorder. One was a randomized, double-blind, placebo-controlled trial of 10-week duloxetine medication treatment, which included 17 patients in duloxetine group and 17 patients in placebo group ( Identifier: NCT00360724); the other one was a randomized, double-blind, placebo-controlled trial of 12-week desvenlafaxine medication treatment, which included 16 patients in desvenlafaxine group and 15 patients in placebo group ( Identifier: NCT01537068). The 24-item Hamilton Depression Rating Scale was used to measure clinical symptoms, and graph theory was employed to examine serotonin-norepinephrine reuptake inhibitor antidepressant treatment effects on the topological properties of whole-brain functional connectome of patients with persistent depressive disorder. We adopted a hierarchical strategy to examine the topological property changes caused by serotonin-norepinephrine reuptake inhibitor antidepressant treatment, calculated their small-worldness, global integration, local segregation and nodal clustering coefficient in turn. Linear regression analysis was used to test associations of treatment, graph properties changes and clinical symptom response. Symptom scores were more significantly reduced after antidepressant than placebo administration (η 2 = 0.18). There was a treatment-by-time effect that optimized the functional connectome in a small-world manner, with increased global integration and increased nodal clustering coefficient in the bilateral thalamus (left thalamus η 2 = 0.21; right thalamus η 2 = 0.23). The nodal clustering coefficient increment of the right thalamus (ratio = 29.86; 95% confidence interval, -4.007 to -0.207) partially mediated the relationship between treatment and symptom improvement, and symptom improvement partially mediated (ratio = 21.21; 95% confidence interval, 0.0243-0.444) the relationship between treatment and nodal clustering coefficient increments of the right thalamus. Our study may indicate a putative mutually reinforcing association between nodal clustering coefficient increment of the right thalamus and symptom improvement from serotonin-norepinephrine reuptake inhibitor antidepressant treatments with duloxetine or desvenlafaxine.

PMID:35592490 | PMC:PMC9113244 | DOI:10.1093/braincomms/fcac100

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease

Fri, 05/20/2022 - 18:00

Sensors (Basel). 2022 Apr 19;22(9):3102. doi: 10.3390/s22093102.


The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain's neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer's patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer's vs. normal controls. The nonfractal-based approach provides a good representation of the brain's neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively.

PMID:35590793 | DOI:10.3390/s22093102

Psychedelic Resting-state Neuroimaging: A Review and Perspective on Balancing Replication and Novel Analyses

Thu, 05/19/2022 - 18:00

Neurosci Biobehav Rev. 2022 May 16:104689. doi: 10.1016/j.neubiorev.2022.104689. Online ahead of print.


Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.

PMID:35588933 | DOI:10.1016/j.neubiorev.2022.104689

Multi-subject task-related fMRI data processing via a two-stage generalized canonical correlation analysis

Thu, 05/19/2022 - 18:00

IEEE Trans Image Process. 2022 May 19;PP. doi: 10.1109/TIP.2022.3159125. Online ahead of print.


Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).

PMID:35588408 | DOI:10.1109/TIP.2022.3159125

Estimations of the weather effects on brain functions using functional MRI: A cautionary note

Thu, 05/19/2022 - 18:00

Hum Brain Mapp. 2022 May 19. doi: 10.1002/hbm.25576. Online ahead of print.


The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross-validation using the resting-state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long-term effects using MRI.

PMID:35586932 | DOI:10.1002/hbm.25576

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

Thu, 05/19/2022 - 18:00

Front Comput Neurosci. 2022 May 2;16:885126. doi: 10.3389/fncom.2022.885126. eCollection 2022.


Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

PMID:35586480 | PMC:PMC9108158 | DOI:10.3389/fncom.2022.885126

Altered Spontaneous Brain Activity in Betel Quid Dependence Chewers: A Resting-State Functional MRI Study With Percent Amplitude of Fluctuation

Thu, 05/19/2022 - 18:00

Front Psychiatry. 2022 May 2;13:830541. doi: 10.3389/fpsyt.2022.830541. eCollection 2022.


OBJECTIVE: This study aimed to investigate brain spontaneous neural activity changes in betel quid dependence (BQD) chewers using the percent amplitude of fluctuation (PerAF) method.

METHODS: This study included 48 BQD chewers. The healthy control (HC) group comprised 35 volunteers who were matched with BQD chewers in age, gender, and educational status. All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological tests. The PerAF method was used to identify BQD-related regional brain activity changes. An independent samples t-test was used to evaluate the PerAF difference across two groups. The association between PerAF changes and clinical features such as BQD scores, duration of BQD, Hamilton Depression Rating Scale-24 item (HAMD-24), and Hamilton Anxiety Rating Scale-14 item (HAMA-14) was evaluated by using Spearman's correlation analysis. It assessed the ability of the PerAF method to distinguish between BQD chewers and HCs using a receiver operating characteristic (ROC) curve.

RESULTS: Compared to the control group, BQD chewers showed decreased PerAF in right anterior cingulate cortex (ACC), right middle frontal gyrus (MFG), right insula, right precuneus, left putamen, left supramarginal gyrus (SMG), and left cerebellum and increased PerAF in right orbitofrontal and left superior temporal gyrus (STG) [P < 0.05, Gaussian random field (GRF) corrected]. PerAF values of the right MFG and right ACC had a significant negative relationship with the duration of BQD (P < 0.05). The average values of PerAF in the left putamen, left cerebellum, and left STG showed significant discriminatory power in distinguishing BQD chewers from HCs, with relatively prime area under the curve (AUC) values.

CONCLUSION: Our findings suggested that betel quid chewing is associated with spontaneous neural activity alterations in the impulsivity areas (MFG and ACC), cognitive (MFG, ACC, precuneus, and the cerebellum), and reward (orbitofrontal, putamen, and insula) systems, which may be correlated with neuropathological mechanisms of BQD. Also, PerAF may be useful as a potential sensitive biomarker for identifying spontaneous brain activity changes in BQD chewers.

PMID:35586413 | PMC:PMC9109957 | DOI:10.3389/fpsyt.2022.830541

Neural Responses of Acupuncture for Treating Functional Dyspepsia: An fMRI Study

Thu, 05/19/2022 - 18:00

Front Neurosci. 2022 May 2;16:819310. doi: 10.3389/fnins.2022.819310. eCollection 2022.


Different acupoints exhibiting similar therapeutic effects are a common phenomenon in acupuncture clinical practice. However, the mechanism underlying this phenomenon remains unclear. This study aimed to investigate the similarities and differences in cerebral activities elicited through stimulation of CV12 and ST36, the two most commonly used acupoints, in the treatment of gastrointestinal diseases, so as to partly explore the mechanism of the different acupoints with similar effects. Thirty-eight eligible functional dyspepsia (FD) patients were randomly assigned into either group A (CV12 group) or group B (ST36 group). Each patient received five acupuncture treatments per week for 4 weeks. The Symptom Index of Dyspepsia (SID), Nepean Dyspepsia Symptom Index (NDSI), and Nepean Dyspepsia Life Quality Index (NDLQI) were used to assess treatment efficacy. Functional MRI (fMRI) scans were performed to detect cerebral activity changes at baseline and at the end of the treatment. The results demonstrated that (1) improvements in NDSI, SID, and NDLQI were found in both group A and group B (p < 0.05). However, there were no significant differences in the improvements of the SID, NDSI, and NDLQI scores between group A and group B (p > 0.05); (2) all FD patients showed significantly increased amplitude of low-frequency fluctuation (ALFF) in the left postcentral gyrus after acupuncture treatment, and the changes of ALFF in the left postcentral gyrus were significantly related to the improvements of SID scores (r = 0.358, p = 0.041); and (3) needling at CV12 significantly decreased the resting-state functional connectivity (rsFC) between the left postcentral gyrus and angular gyrus, caudate, middle frontal gyrus (MFG), and cerebellum, while needling at ST36 significantly increased the rsFC between the left postcentral gyrus with the precuneus, superior frontal gyrus (SFG), and MFG. The results indicated that CV12 and ST36 shared similar therapeutic effects for dyspepsia, with common modulation on the activity of the postcentral gyrus in FD patients. However, the modulatory pattern on the functional connectivity of the postcentral gyrus was different. Namely, stimulation of CV12 primarily involved the postcentral gyrus-reward network, while stimulation of ST36 primarily involved the postcentral gyrus-default mode network circuitry.

PMID:35585920 | PMC:PMC9108289 | DOI:10.3389/fnins.2022.819310

Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus

Thu, 05/19/2022 - 18:00

Front Neurosci. 2022 May 2;16:839194. doi: 10.3389/fnins.2022.839194. eCollection 2022.


OBJECTIVE: To investigate morphological and functional alterations within gray matter (GM) in female patients with neuropsychiatric systemic lupus (NPSLE) and to explore their clinical significance.

METHODS: 54 female patients with SLE (30 NPSLE and 24 non-NPSLE) and 32 matched healthy controls were recruited. All subjects received a quantitative MRI scan (FLAIR, 3DT1, resting-state functional MRI). GM volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree of centrality (DC) were obtained. Between-group comparison, clinical correlation, and discrimination of NPSLE from non-NPSLE were achieved by voxel-based analysis, cerebellar seed-based functional connectivity analysis, regression analysis, and support vector machine (SVM), respectively.

RESULTS: Patients with NPSLE showed overt subcortical GM atrophy without significantly abnormal brain functions in the same region compared with controls. The dysfunction within the left superior temporal gyri (L-STG) was found precede the GM volumetric loss. The function of the nodes in default mode network (DMN) and salience network (SN) were weakened in NPSLE patients compared to controls. The function of the cerebellar posterior lobes was significantly activated in non-NPSLE patients but attenuated along with GM atrophy and presented higher connectivity with L-STG and DMN in NPSLE patients, while the variation of the functional activities in the sensorimotor network (SMN) was the opposite. These structural and functional alterations were mainly correlated with disease burden and anti-phospholipid antibodies (aPLs) (r ranges from -1.53 to 1.29). The ReHos in the bilateral cerebellar posterior lobes showed high discriminative power in identifying patients with NPSLE with accuracy of 87%.

CONCLUSION: Patients with NPSLE exhibit both structural and functional alterations in the GM of the brain, which especially involved the deep GM, the cognitive, and sensorimotor regions, reflecting a reorganization to compensate for the disease damage to the brain which was attenuated along with pathologic burden and cerebral vascular risk factors. The GM within the left temporal lobe may be one of the direct targets of lupus-related inflammatory attack. The function of the cerebellar posterior lobes might play an essential role in compensating for cortical functional disturbances and may contribute to identifying patients with suspected NPSLE in clinical practice.

PMID:35585919 | PMC:PMC9108669 | DOI:10.3389/fnins.2022.839194

Brain Functional Alteration at Different Stages of Neuropathic Pain With Allodynia and Emotional Disorders

Thu, 05/19/2022 - 18:00

Front Neurol. 2022 May 2;13:843815. doi: 10.3389/fneur.2022.843815. eCollection 2022.


Neuropathic pain (NeuP), a challenging medical condition, has been suggested by neuroimaging studies to be associated with abnormalities of neural activities in some brain regions. However, aberrancies in brain functional alterations underlying the sensory-discriminative abnormalities and negative emotions in the setting of NeuP remain unexplored. Here, we aimed to investigate the functional alterations in neural activity relevant to pain as well as pain-related depressive-like and anxiety-like behaviors in NeuP by combining amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) analyses methods based on resting-state functional magnetic resonance imaging (rs-fMRI). A rat model of NeuP was established via chronic constriction injury (CCI) of the sciatic nerve. Results revealed that the robust mechanical allodynia occurred early and persisted throughout the entire observational period. Depressive and anxiety-like behaviors did not appear until 4 weeks after injury. When the maximum allodynia was apparent early, CCI rats exhibited decreased ALFF and DC values in the left somatosensory and nucleus accumbens shell (ACbSh), respectively, as compared with sham rats. Both values were significantly positively correlated with mechanical withdrawal thresholds (MWT). At 4 weeks post-CCI, negative emotional states were apparent and CCI rats were noted to exhibit increased ALFF values in the left somatosensory and medial prefrontal cortex (mPFC) as well as increased DC values in the right motor cortex, as compared with sham rats. At 4 weeks post-CCI, ALFF values in the left somatosensory cortex and DC values in the right motor cortex were noted to negatively correlate with MWT and exhibition of anxiety-like behavior on an open-field test (OFT); values were found to positively correlate with the exhibition of depressive-like behavior on forced swimming test (FST). The mPFC ALFF values were found to negatively correlate with the exhibition of anxiety-like behavior on OFT and positively correlate with the exhibition of depressive-like behavior on FST. Our findings detail characteristic alterations of neural activity patterns induced by chronic NeuP and underscore the important role of the left somatosensory cortex, as well as its related networks, in the mediation of subsequent emotional dysregulation due to NeuP.

PMID:35585842 | PMC:PMC9108233 | DOI:10.3389/fneur.2022.843815

The effect of mindfulness training on resting-state networks in pre-adolescent children with sub-clinical anxiety related attention impairments

Wed, 05/18/2022 - 18:00

Brain Imaging Behav. 2022 May 18. doi: 10.1007/s11682-022-00673-2. Online ahead of print.


Mindfulness training has been associated with improved attention and affect regulation in preadolescent children with anxiety related attention impairments, however little is known about the underlying neurobiology. This study sought to investigate the impact of mindfulness training on functional connectivity of attention and limbic brain networks in pre-adolescents. A total of 47 children with anxiety and/or attention issues (aged 9-11 years) participated in a 10-week mindfulness intervention. Anxiety and attention measures and resting-state fMRI were completed at pre- and post-intervention. Sustained attention was measured using the Conners Continuous Performance Test, while the anxiety levels were measured using the Spence Children's Anxiety Scale. Functional networks were estimated using independent-component analysis, and voxel-based analysis was used to determine the difference between the time-points to identify the effect of the intervention on the functional connectivity. There was a significant decrease in anxiety symptoms and improvement in attention scores following the intervention. From a network perspective, the results showed increased functional connectivity post intervention in the salience and fronto-parietal networks as well as the medial-inferior temporal component of the default mode network. Positive correlations were identified in the fronto-parietal network with Hit Response Time and the Spence Children's Anxiety Scale total and between the default mode network and Hit Response Time. A 10-week mindfulness intervention in children was associated with a reduction in anxiety related attention impairments, which corresponded with concomitant changes in functional connectivity.

PMID:35585445 | DOI:10.1007/s11682-022-00673-2

Alterations in Structural and Functional Connectivity in ADHD: Implications for Theories of ADHD

Wed, 05/18/2022 - 18:00

Curr Top Behav Neurosci. 2022 May 19. doi: 10.1007/7854_2022_345. Online ahead of print.


Attention-Deficit/Hyperactivity Disorder (ADHD) is increasingly viewed as a disorder of brain connectivity. We review connectivity-based theories of ADHD including the default mode network (DMN) interference and multiple network hypotheses. We outline the main approaches used to study brain connectivity in ADHD: diffusion tensor imaging and resting-state functional connectivity. We discuss the basic principles underlying these methods and the main analytical approaches used and consider what the findings have told us about connectivity alterations in ADHD. The most replicable finding in the diffusion tensor imaging literature on ADHD is lower fractional anisotropy in the corpus callosum, a key commissural tract which connects the brain's hemispheres. Meta-analyses of resting-state functional connectivity studies have failed to identify spatial convergence across studies, with the exception of meta-analyses focused on specific networks which have reported within-network connectivity alterations in the DMN and between the DMN and the fronto-parietal control and salience networks. Overall, methodological heterogeneity between studies and differences in sample characteristics are major barriers to progress in this area. In addition, females, adults and medication-naïve/unmedicated individuals are under-represented in connectivity studies, comorbidity needs to be assessed more systematically, and longitudinal research is needed to investigate whether ADHD is characterized by maturational delays in connectivity.

PMID:35583796 | DOI:10.1007/7854_2022_345

BOLD turnover in task-free state: variation among brain areas and effects of age and human leukocyte antigen (HLA) DRB1*13

Wed, 05/18/2022 - 18:00

Exp Brain Res. 2022 May 18. doi: 10.1007/s00221-022-06382-y. Online ahead of print.


Blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) is frequently used as a proxy for underlying neural activity. Although this is a plausible assumption for experiments where a task is performed, it may not hold to the same degree for conditions of fMRI recording in a task-free, "resting" state where neural synaptic events are weak and, hence, neurovascular coupling and endothelial vascular factors become more prominent (Hillman Annu Rev Neurosci 37:161-181, 2014, 10.1146/annurev-neuro-071013-014111). Here we investigated the magnitude of change of BOLD in consecutive samples over the acquisition time period (turnover of BOLD, "TBOLD") by first-order differencing of single-voxel BOLD time series acquired in 70 areas of the cerebral cortex of 57 cognitively healthy women in a task-free resting state. More specifically, we evaluated (a) the variation of TBOLD among different cortical areas, (b) its dependence on age, and (c) its dependence on the presence (or absence) of the neuroprotective Human Leukocyte Antigen (HLA) gene DRB1*13 (DRB1*13:02 and DRB1*13:01). We found that TBOLD (a) varied substantially by 2.2 × among cortical areas, being highest in parahippocampal and entorhinal areas and lowest in parietal-occipital areas, (b) was significantly reduced in DRB1*13 carriers across cortical areas (from ~ 15% reduction in orbitofrontal cortex to 2% reduction in cuneus), and (c) increased with age in noncarriers of DRB1*13 but decreased with age in DRB1*13 carriers. These findings document significant dependencies of TBOLD on cortical area location, HLA DRB1*13 and age.

PMID:35583670 | DOI:10.1007/s00221-022-06382-y

Correction to: Frontopolar tDCS induces frequency-dependent changes of spontaneous low-frequency fluctuations: a resting-state fMRI study

Wed, 05/18/2022 - 18:00

Cereb Cortex. 2022 May 18:bhac228. doi: 10.1093/cercor/bhac228. Online ahead of print.


PMID:35583162 | DOI:10.1093/cercor/bhac228

Resting-state functional connectivity and spontaneous brain activity in early-onset bipolar disorder: A review of functional Magnetic Resonance Imaging studies

Tue, 05/17/2022 - 18:00

J Affect Disord. 2022 May 14:S0165-0327(22)00563-8. doi: 10.1016/j.jad.2022.05.055. Online ahead of print.


BACKGROUND: Early-onset bipolar disorder (BD) is a complex psychiatric illness characterized by mood swings, irritability and functional impairments. To improve our understanding of the pathophysiology of the disorder, we collected the existing resting-state functional Magnetic Resonance Imaging (rs-fMRI) studies exploring resting-state functional connectivity (rs-FC) and spontaneous activity alterations in children and adolescents with BD.

METHODS: A search on PubMed, Web of Science and Scopus was conducted to identify all the relevant rs-fMRI investigations conducted in early-onset BD. A total of 14 studies employing different methodological approaches to explore rs-FC and spontaneous activity in early-onset BD were included (independent component analysis, n = 1; seed-based analysis, n = 7; amplitude of low frequency fluctuations analysis, n = 2; regional homogeneity analysis, n = 4).

RESULTS: Overall, the studies showed abnormalities within the Default Mode Network (DMN) and between the DMN and the Salience Network (SN). Moreover, widespread alterations in rs-FC and spontaneous brain activity within and between cortico-limbic structures, involving primarily the occipital and frontal lobes, amygdala, hippocampus, insula, thalamus and striatum were also reported.

LIMITATIONS: The small sample sizes, the use of medications, the presence of comorbidities and the heterogeneity in methods hamper the integration of the study findings.

CONCLUSIONS: Early-onset BD seems to be characterized by selective rs-FC and spontaneous activity dysfunctions in DMN and SN and in the cortico-limbic and cortico-striatal circuits, which could explain the emotive and cognitive deficits observed in this disabling psychiatric illness.

PMID:35580695 | DOI:10.1016/j.jad.2022.05.055

A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data

Tue, 05/17/2022 - 18:00

Med Image Anal. 2022 May 7;79:102471. doi: 10.1016/ Online ahead of print.


Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. Typically, the brain is parcellated into regions of interest (ROIs) and modelled as a graph where each ROI represents a node and association measures between ROI-specific blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their success in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs) in order to learn from both the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging interactions between ROI-wise dynamics with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database, as well as in the smaller Human Connectome Project (HCP) dataset, both in a unimodal and in a multimodal fashion. We also demonstrate that out architecture contains explainability-related features which easily map to realistic neurobiological insights. We suggest that this model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

PMID:35580429 | DOI:10.1016/

Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease

Mon, 05/16/2022 - 18:00

BMC Neurol. 2022 May 16;22(1):179. doi: 10.1186/s12883-022-02705-2.


BACKGROUND: Disruption of brain functional connectivity has been detected after stroke, but whether it also occurs in moyamoya disease (MMD) is unknown. Impaired functional connectivity is always correlated with abnormal white matter fibers. Herein, we used multimodal imaging techniques to explore the changes in brain functional and structural connectivity in MMD patients.

METHODS: We collected structural images, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging for each subject. Cognitive functions of MMD patients were evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Trail Making Test parts A and B (TMT-A/-B). We calculated the functional connectivity for every paired region using 90 regions of interest from the Anatomical Automatic Labeling Atlas and then determined the differences between MMD patients and HCs. We extracted the functional connectivity of paired brain regions with significant differences between the two groups. Correlation analyses were then performed between the functional connectivity and variable cognitive functions. To explore whether the impaired functional connectivity and cognitive performances were attributed to the destruction of white matter fibers, we further analyzed fiber integrity using tractography between paired regions that were correlated with cognition.

RESULTS: There was lower functional connectivity in MMD patients as compared to HCs between the bilateral inferior frontal gyrus, between the bilateral supramarginal gyrus, between the left supplementary motor area (SMA) and the left orbital part of the inferior frontal gyrus (IFGorb), and between the left SMA and the left middle temporal gyrus (P < 0.01, FDR corrected). The decreased functional connectivity between the left SMA and the left IFGorb was significantly correlated with the MMSE (r = 0.52, P = 0.024), MoCA (r = 0.60, P = 0.006), and TMT-B (r = -0.54, P = 0.048) in MMD patients. White matter fibers were also injured between the SMA and IFGorb in the left hemisphere and were positively correlated with reduced functional connectivity.

CONCLUSIONS: Brain functional and structural connectivity between the supplementary motor area and inferior frontal gyrus in the left hemisphere are damaged in MMD. These findings could be useful in the evaluation of disease progression and prognosis of MMD.

PMID:35578209 | DOI:10.1186/s12883-022-02705-2

Clinical evaluation and resting state fMRI analysis of virtual reality based training in Parkinson's disease through a randomized controlled trial

Mon, 05/16/2022 - 18:00

Sci Rep. 2022 May 16;12(1):8024. doi: 10.1038/s41598-022-12061-3.


There are few studies investigating the short-term effects of Virtual Reality based Exergaming (EG) on motor and cognition simultaneously and pursue the brain functional activity changes after these interventions in patients with Parkinson's Disease (PD). The purpose of this study was to investigate the synergistic therapeutic effects of Virtual Reality based EG on motor and cognitive symptoms in PD and its possible effects on neuroplasticity. Eligible patients with the diagnosis of PD were randomly assigned to one of the two study groups: (1) an experimental EG group, (2) an active control Exercise Therapy (ET) group. All patients participated in a 4-week exercise program consisting of 12 treatment sessions. Every session lasted 60 min. Participants underwent a motor evaluation, extensive neuropsychological assessment battery and rs-fMRI before and after the interventions. Thirty patients fulfilled the inclusion criteria and were randomly assigned to the EG and ET groups. After the dropouts, 23 patients completed the assessments and interventions (11 in EG, 13 in ET). Within group analysis showed significant improvements in both groups. Between group comparisons considering the interaction of group × time effect, showed superiority of EG in terms of general cognition, delayed visual recall memory and Boston Naming Test. These results were consistent in the within-group and between-group analysis. Finally, rs-fMRI analysis showed increased activity in the precuneus region in the time × group interaction in the favor of EG group. EG can be an effective alternative in terms of motor and cognitive outcomes in patients with PD. Compared to ET, EG may affect brain functional connectivity and can have beneficial effects on patients' cognitive functions and motor symptoms. Whenever possible, using EG and ET in combination, may have the better effects on patients daily living and patients can benefit from the advantages of both interventions.

PMID:35577874 | DOI:10.1038/s41598-022-12061-3

Classification of tic disorders based on functional MRI by machine learning: a study protocol

Mon, 05/16/2022 - 18:00

BMJ Open. 2022 May 16;12(5):e047343. doi: 10.1136/bmjopen-2020-047343.


INTRODUCTION: Tic disorder (TD) is a common neurodevelopmental disorder in children, and it can be categorised into three subtypes: provisional tic disorder (PTD), chronic motor or vocal TD (CMT or CVT), and Tourette syndrome (TS). An early diagnostic classification among these subtypes is not possible based on a new-onset tic symptom. Machine learning tools have been widely used for early diagnostic classification based on functional MRI (fMRI). However, few machine learning models have been built for the diagnostic classification of patients with TD. Therefore, in the present study, we will provide a study protocol that uses the machine learning model to make early classifications of the three different types of TD.

METHODS AND ANALYSIS: We planned to recruit 200 children aged 6-9 years with new-onset tic symptoms and 100 age-matched and sex-matched healthy controls under resting-state MRI scanning. Based on the neuroimaging data of resting-state fMRI, the support vector machine (SVM) model will be built. We planned to construct an SVM model based on functional connectivity for the early diagnosis classification of TD subtypes (including PTD, CMT/CVT, TS).

ETHICS AND DISSEMINATION: This study was approved by the ethics committee of Beijing Children's Hospital. The trial results will be submitted to peer-reviewed journals for publication.


PMID:35577466 | DOI:10.1136/bmjopen-2020-047343

A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

Mon, 05/16/2022 - 18:00

Neuroimage. 2022 May 13:119279. doi: 10.1016/j.neuroimage.2022.119279. Online ahead of print.


The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.

PMID:35577026 | DOI:10.1016/j.neuroimage.2022.119279