Most recent paper

Aberrant Static and Dynamic Functional Network Connectivity in Acute Mild Traumatic Brain Injury with Cognitive Impairment

Tue, 08/31/2021 - 18:00

Clin Neuroradiol. 2021 Aug 31. doi: 10.1007/s00062-021-01082-6. Online ahead of print.


PURPOSE: This study aimed to investigate differences in static and dynamic functional network connectivity (FNC) and explore their association with neurocognitive performance in acute mild traumatic brain injury (mTBI).

METHODS: A total of 76 patients with acute mTBI and 70 age-matched and sex-matched healthy controls were enrolled (age 43.79 ± 10.22 years vs. 45.63 ± 9.49 years; male/female: 34/42 vs. 38/32; all p > 0.05) and underwent resting-state functional magnetic resonance imaging (fMRI) scan (repetition time/echo time = 2000/30 ms, 230 volumes). Independent component analysis was conducted to evaluate static and dynamic FNC patterns on the basis of nine resting-state networks, namely, auditory network (AUDN), dorsal attention network (dAN), ventral attention network (vAN), default mode network (DMN), left frontoparietal network (LFPN), right frontoparietal network (RFPN), somatomotor network (SMN), visual network (VN), and salience network (SN). Spearman's correlation among aberrances in FNC values, and Montreal cognitive assessment (MoCA) scores was further measured in mTBI.

RESULTS: Compared with controls, patients with mTBI showed wide aberrances of static FNC, such as reduced FNC in DMN-vAN and VN-vAN pairs. The mTBI patients exhibited aberrant dynamic FNC in state 2, involving reduced FNC aberrance in the vAN with AUDN, VN with DMN and dAN, and SN with SMN and vAN. Reduced dFNC in the SN-vAN pair was negatively correlated with the MoCA score.

CONCLUSION: Our findings suggest that aberrant static and dynamic FNC at the acute stage may contribute to cognitive symptoms, which not only may expand knowledge regarding FNC cognition relations from the static perspective but also from the dynamic perspective.

PMID:34463779 | DOI:10.1007/s00062-021-01082-6

Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation

Tue, 08/31/2021 - 18:00

Elife. 2021 Aug 31;10:e68980. doi: 10.7554/eLife.68980. Online ahead of print.


Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes, however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal components analysis (PCA) and characterized the cluster-specific pupil-fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil-fMRI relationship.

PMID:34463612 | DOI:10.7554/eLife.68980

A multi-site, multi-disorder resting-state magnetic resonance image database

Tue, 08/31/2021 - 18:00

Sci Data. 2021 Aug 30;8(1):227. doi: 10.1038/s41597-021-01004-8.


Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants ("traveling subjects") visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

PMID:34462444 | DOI:10.1038/s41597-021-01004-8

In vivo evidence of functional disconnection between brainstem monoaminergic nuclei and brain networks in multiple sclerosis

Mon, 08/30/2021 - 18:00

Mult Scler Relat Disord. 2021 Aug 24;56:103224. doi: 10.1016/j.msard.2021.103224. Online ahead of print.


BACKGROUND: brainstem monoaminergic (dopaminergic, noradrenergic, and serotoninergic) nuclei (BrMn) contain a variety of ascending neurons that diffusely project to the whole brain, crucially regulating normal brain function. BrMn are directly affected in multiple sclerosis (MS) by inflammation and neurodegeneration. Moreover, inflammation reduces the synthesis of monoamines. Aberrant monoaminergic neurotransmission contributes to the pathogenesis of MS and explains some clinical features of MS. We used resting-state functional MRI (RS-fMRI) to characterize abnormal patterns of BrMn functional connectivity (FC) in MS.

METHODS: BrMn FC was studied with multi-echo RS-fMRI in n = 68 relapsing-remitting MS patients and n = 39 healthy controls (HC), by performing a seed-based analysis, after producing standard space seed masks of the BrMn. FC was assessed between ventral tegmental area (VTA), locus coeruleus (LC), median raphe (MR), dorsal raphe (DR), and the rest of the brain and compared between MS patients and HC. Between-group comparisons were carried out only within the main effect observed in HC, setting p<0.05 family-wise-error corrected (FWE).

RESULTS: in HC, VTA displayed FC with the core regions of the default-mode network. As compared to HC, MS patients showed altered FC between VTA and posterior cingulate cortex (p<0.05FWE). LC displayed FC with core regions of the executive-control network with a reduced functional connection between LC and right prefrontal cortex in MS patients (p<0.05FWE). Raphe nuclei was functionally connected with cerebellar cortex, with a significantly lower FC between these nuclei and cerebellum in MS patients, as compared to HC (p<0.05FWE).

CONCLUSIONS: our study demonstrated in MS patients a functional disconnection between BrMn and cortical/subcortical efferent targets of central brain networks, possibly due to a loss or a dysregulation of BrMn neurons. This adds new information about how monoaminergic systems contribute to MS pathogenesis and suggests new potential therapeutic targets.

PMID:34461571 | DOI:10.1016/j.msard.2021.103224

Functional magnetic resonance imaging in neurosurgery

Mon, 08/30/2021 - 18:00

Zh Nevrol Psikhiatr Im S S Korsakova. 2021;121(7):118-123. doi: 10.17116/jnevro2021121071118.


The review of publications on functional magnetic resonance imaging (fMRI) and its practical application in neurosurgery is presented. Advantages and disadvantages are selected taking pathogenesis into account. Results of surgical treatment with use of functional navigation are described. Separate attention is paid to fMRI precision by its comparing with direct cortical stimulation. New resting-state method of visualization is observed. Practical advices are given of fMRI application in neurooncology and surgery of arteriovenous malformations.

PMID:34460167 | DOI:10.17116/jnevro2021121071118

Belief in Communism and Theory of Mind

Mon, 08/30/2021 - 18:00

Front Psychol. 2021 Aug 5;12:697251. doi: 10.3389/fpsyg.2021.697251. eCollection 2021.


A belief in communism refers to the unquestionable trust and belief in the justness of communism. Although former studies have discussed the political aim and social value of communism, the cognitive neural basis of a belief in communism remains largely unknown. In this study, we determined the behavioral and neural correlates between a belief in communism and a theory of mind (ToM). For study 1, questionnaire scores were measured and for study 2, regional homogeneity (ReHo) and resting-state functional connectivity (rsFC) were used as an index for resting-state functional MRI (rs-fMRI), as measured by the Belief in Communism Scale (BCS). The results showed that a belief in communism is associated with higher ReHo in the left thalamus and lower ReHo in the left medial frontal gyrus (MFG). Furthermore, the results of the rsFC analysis revealed that strength of functional connectivity between the left thalamus and the bilateral precuneus is negatively associated with a belief in communism. Hence, this study provides evidence that spontaneous brain activity in multiple regions, which is associated with ToM capacity, contributes to a belief in communism.

PMID:34456814 | PMC:PMC8386467 | DOI:10.3389/fpsyg.2021.697251

The Value of Brain Resting-State Functional Magnetic Resonance Imaging on Image Registration Algorithm in Analyzing Abnormal Changes of Neuronal Activity in Patients with Type 2 Diabetes

Mon, 08/30/2021 - 18:00

Contrast Media Mol Imaging. 2021 Aug 13;2021:6951755. doi: 10.1155/2021/6951755. eCollection 2021.


The aim of this paper was to analyze the application value of resting-state functional magnetic resonance imaging (FMRI) parameters and rigid transformation algorithm in patients with type 2 diabetes (T2DM), which could provide a theoretical basis for the registration application of FMRI. 107 patients confirmed pathologically as T2DM and 51 community medical healthy volunteers were selected and divided into an experimental group and a control group, respectively. Besides, all the subjects were scanned with FMRI. Then, the rigid transformation-principal axis algorithm (RT-PAA), Levenberg-Marquardt iterative closest point (LMICP), and Demons algorithm were applied to magnetic resonance image registration. It was found that RT-PAA was superior to LMICP and Demons in image registration. The amplitude of low-frequency fluctuation (ALFF) values of the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients from the experimental group were lower than those of the control group (P < 0.05). The Montreal cognitive assessment (MoCA) score was extremely negatively correlated with the ALFF of the left middle temporal gyrus (r = -0.451 and P < 0.001) and highly positively associated with the ALFF of the right posterior cerebellar lobe (r = -0.484 and P < 0.001). In addition, the MoCA score of patients had a dramatically negative correlation with the ALFF of the left middle temporal gyrus (r = -0.602 and P < 0.001) and had a greatly positive correlation with the ALFF of the right posterior cerebellar lobe (r = -0.516 and P < 0.001). The results showed that RT-PAA based on rigid transformation in this study had a good registration effect on magnetic resonance images. Compared with healthy volunteers, the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients with T2DM showed abnormal neuronal changes and reduced cognitive function.

PMID:34456650 | PMC:PMC8380164 | DOI:10.1155/2021/6951755

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment

Sun, 08/29/2021 - 18:00

Clin Neurophysiol. 2021 Aug 16;132(10):2540-2550. doi: 10.1016/j.clinph.2021.06.036. Online ahead of print.


OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment, and Alzheimer's Disease individuals.

METHODS: A novel mapping function based on the B distribution has been developed to map correlation matrices to robust functional connectivity. The node2vec algorithm is applied to the functional connectivity to produce node embeddings. The concatenation of these embedding has been used to derive the patients' feature vectors for further feeding into the Support Vector Machine and Logistic Regression classifiers.

RESULTS: The experimental results indicate promising results in the complex four-class classification problem with an accuracy rate of 97.73% and a quadratic kappa score of 96.86% for the Support Vector Machine. These values are 97.32% and 96.74% for Logistic Regression.

CONCLUSION: This study presents an accurate automated method for dementia classification. Default Mode Network and Dorsal Attention Network have been found to demonstrate a significant role in the classification method.

SIGNIFICANCE: A new mapping function is proposed in this study, the mapping function improves accuracy by 10-11% in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

PMID:34455312 | DOI:10.1016/j.clinph.2021.06.036

A naturalistic paradigm to investigate post-encoding neural activation patterns in relation to subsequent voluntary and intrusive recall of distressing events

Sat, 08/28/2021 - 18:00

Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Aug 25:S2451-9022(21)00231-7. doi: 10.1016/j.bpsc.2021.08.006. Online ahead of print.


BACKGROUND: While neuroimaging has provided insights into the formation of episodic memories in relation to voluntary memory recall, less is known about neural mechanisms that cause memories to occur involuntarily, for example as intrusive memories of trauma. Here we investigated brain activity shortly after viewing distressing events as a function of whether memories for those events later intruded involuntarily. The post-encoding period is particularly important because it is a period when clinical interventions could be applied.

METHODS: Thirty-two healthy volunteers underwent functional Magnetic Resonance Imaging (fMRI) while viewing distressing film clips, interspersed with five minutes of awake (post-encoding) rest. Voluntary memories of the films were assessed using free recall and verbal and visual recognition tests after a week, while intrusive (involuntary) memories were recorded in a diary throughout that week.

RESULTS: When analysing fMRI responses related to watching the films, we replicated findings that those "hotspots" (salient moments within the films) that would later become intrusive memories elicited higher activation in parts of the brain's salience network. Surprisingly, while the post-encoding persistence of multi-voxel correlation structures associated with entire film clips predicted subsequent voluntary recall, there was no evidence that they predicted subsequent intrusions.

CONCLUSIONS: Results replicate findings regarding the formation of intrusive memories during encoding, and extend findings regarding the consolidation of information in post-encoding rest in relation to voluntary memory. While we provided a first step using a naturalistic paradigm, further research is needed to elucidate the role of post-encoding neural processes in the development of intrusive memories.

PMID:34454167 | DOI:10.1016/j.bpsc.2021.08.006

Cerebellar Transcranial Direct Current Stimulation Reconfigures Brain Networks Involved in Motor Execution and Mental Imagery

Sat, 08/28/2021 - 18:00

Cerebellum. 2021 Aug 28. doi: 10.1007/s12311-021-01322-y. Online ahead of print.


Transcranial direct current stimulation (tDCS) is growingly applied to the cerebellum to modulate the activity of cerebellar circuitry, affecting both motor and cognitive performances in a polarity-specific manner. The remote effects of tDCS are mediated in particular via the dentato-thalamo-cortical pathway. We showed recently that tDCS of the cerebellum exerts dynamic effects on resting state networks. We tested the neural hypothesis that tDCS reconfigurates brain networks involved in motor execution (ME) and motor mental imagery (MMI). We combined tDCS applied over the right cerebellum and fMRI to investigate tDCS-induced reconfiguration of ME- and MMI-related networks using a randomized, sham-controlled design in 21 right-handed healthy volunteers. Subjects were instructed to draw circles at comfortable speed and to imagine drawing circles with their right hand. fMRI data were recorded after real anodal stimulation (1.5 mA, 20 min) or sham tDCS. Real tDCS compared with SHAM specifically reconfigurated the functional links between the main intrinsic connected networks, especially the central executive network, in relation with lobule VII, and the salience network. The right cerebellum mainly influenced prefrontal and anterior cingulate areas in both tasks, and improved the overt motor performance. During MMI, the cerebellum also modulated the default-mode network and associative visual areas. These results demonstrate that tDCS of the cerebellum represents a novel tool to modulate cognitive brain networks controlling motor execution and mental imagery, tuning the activity of remote cortical regions. This approach opens novel doors for the non-invasive neuromodulation of disorders involving cerebello-thalamo-cortical paths.

PMID:34453688 | DOI:10.1007/s12311-021-01322-y

Professional chess expertise modulates whole brain functional connectivity pattern homogeneity and couplings

Sat, 08/28/2021 - 18:00

Brain Imaging Behav. 2021 Aug 28. doi: 10.1007/s11682-021-00537-1. Online ahead of print.


Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in chess players remains unclear. It could provide more convincing evidence for establishing the relationship between long-term chess practice and brain function changes. In this study, we employed newly developed whole brain functional connectivity pattern homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 28 chess master players and 27 healthy novices. Seed-based functional connectivity analysis was used to identify the alteration of corresponding functional couplings. FcHo analysis revealed significantly increased whole brain functional connectivity pattern similarity in anterior cingulate cortex (ACC), anterior middle temporal gyrus (aMTG), primary visual cortex (V1), and decreased FcHo in thalamus and precentral gyrus in chess players. Resting-state functional connectivity analyses identified chess players showing decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo values of thalamus were significantly negatively correlated with the training time. Our findings provide new evidences for the important roles of ACC, aMTG, V1, thalamus and precentral gyrus in chess players. The findings also indicate that long-term professional chess training may enhance the semantic and episodic processing, efficiency of visual-motor transformation, and cognitive ability.

PMID:34453664 | DOI:10.1007/s11682-021-00537-1

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Sat, 08/28/2021 - 18:00

Sensors (Basel). 2021 Aug 4;21(16):5256. doi: 10.3390/s21165256.


The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

PMID:34450699 | DOI:10.3390/s21165256

Categorizing cortical dysplasia lesions for surgical outcome using network functional connectivity

Fri, 08/27/2021 - 18:00

J Neurosurg Pediatr. 2021 Aug 27:1-9. doi: 10.3171/2021.5.PEDS20990. Online ahead of print.


OBJECTIVE: Focal cortical dysplasia (FCD) is often associated with drug-resistant epilepsy, leading to a recommendation to surgically remove the seizure focus. Predicting outcome for resection of FCD is challenging, requiring a new approach. Lesion-symptom mapping is a powerful and broadly applicable method for linking neurological symptoms or outcomes to damage to particular brain regions. In this work, the authors applied lesion network mapping, an expansion of the traditional approach, to search for the association of lesion network connectivity with surgical outcomes. They hypothesized that connectivity of lesion volumes, preoperatively identified by MRI, would associate with seizure outcomes after surgery in a pediatric cohort with FCD.

METHODS: This retrospective study included 21 patients spanning the ages of 3 months to 17.7 years with FCD lesions who underwent surgery for drug-resistant epilepsy. The mean brain-wide functional connectivity map of each lesion volume was assessed across a database of resting-state functional MRI data from healthy children (spanning approximately 2.9 to 18.9 years old) compiled at the authors' institution. Lesion connectivity maps were averaged across age and sex groupings from the database and matched to each patient. The authors sought to associate voxel-wise differences in these maps with subject-specific surgical outcome (seizure free vs persistent seizures).

RESULTS: Lesion volumes with persistent seizures after surgery tended to have stronger connectivity to attention and motor networks and weaker connectivity to the default mode network compared with lesion volumes with seizure-free surgical outcome.

CONCLUSIONS: Network connectivity-based lesion-outcome mapping may offer new insight for determining the impact of lesion volumes discerned according to both size and specific location. The results of this pilot study could be validated with a larger set of data, with the ultimate goal of allowing examination of lesions in patients with FCD and predicting their surgical outcomes.

PMID:34450591 | DOI:10.3171/2021.5.PEDS20990

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

Fri, 08/27/2021 - 18:00

Neuroimage. 2021 Aug 24:118513. doi: 10.1016/j.neuroimage.2021.118513. Online ahead of print.


A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

PMID:34450262 | DOI:10.1016/j.neuroimage.2021.118513

Dysconnectivity of a brain functional network was associated with blood inflammatory markers in depression

Fri, 08/27/2021 - 18:00

Brain Behav Immun. 2021 Aug 24:S0889-1591(21)00516-X. doi: 10.1016/j.bbi.2021.08.226. Online ahead of print.


OBJECTIVE: There is increasing evidence for a subgroup of major depressive disorder (MDD) associated with heightened peripheral blood inflammatory markers. In this study, we aimed to understand the mechanistic brain-immune axis in inflammation-linked depression by investigating associations between functional connectivity (FC) of brain networks and peripheral blood immune markers in depression.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) and peripheral blood inflammatory markers (C-reactive protein; CRP, interleukin-6; IL-6 and immune cells) were collected on N=46 healthy controls (HC; CRP ≤ 3mg/L) and N=83 cases of depression, stratified further into low CRP cases (loCRP cases; ≤ 3 mg/L; N=50) and high CRP cases (hiCRP cases; > 3 mg/L; N=33). In a two-part analysis, network-based statistics (NBS) was firstly used to ascertain whole-brain FC differences in HC vs hiCRP cases. Secondly, we investigated the association between this network of interconnected brain regions and continuous measures of peripheral CRP (N=83), IL-6 (N=72), neutrophils and CD4+ T-cells (N=36) in depression cases only.

RESULTS: Case-control NBS testing revealed a single network of abnormally attenuated FC in the high CRP depression cases compared to healthy controls. Connections within this network were mainly between brain regions located in the left insula/frontal operculum and posterior cingulate cortex, which were assigned to ventral attention and default mode canonical fMRI networks respectively. Within-group analysis across all depression cases, secondarily demonstrated that FC within the identified network significantly negatively scaled with CRP, IL-6 and neutrophils.

CONCLUSIONS: The findings suggest that inflammation is associated with disruption of functional connectivity within a brain network deemed critical for interoceptive signalling, e.g. accurate communication of peripheral bodily signals such as immune states to the brain, with implications for the pathogenesis of inflammation-linked depression.

PMID:34450247 | DOI:10.1016/j.bbi.2021.08.226

Asymmetry of the insula-sensorimotor circuit in Parkinson's disease

Fri, 08/27/2021 - 18:00

Eur J Neurosci. 2021 Aug 27. doi: 10.1111/ejn.15432. Online ahead of print.


Patients with Parkinson's disease (PD) experience motor and non-motor symptoms, suggesting alterations of the motor and/or limbic system, or more probably of their communications. We hypothesized that the communication between the insula (part of the limbic system) and sensorimotor cortex in PD is altered and hemispheric asymmetric. Furthermore, that this asymmetry relates to non-motor symptoms, and specifically, that apathy-related asymmetry is unique to PD. To test these hypotheses, we used a novel multivariate time-frequency analysis method applied to resting-state functional MRI data of 28 controls and 25 participants with PD measured in their OFF medication state. The analysis infers directionality of coupling, that is, afferent or efferent, among four anatomical regions, thus defining directed pathways of information flow, which enables the extension of symmetry measures to include directionality. A major right asymmetry reduction of the dorsal-posterior insula efferent and a slight bilateral increase of insula afferent pathways were observed in participants with PD versus controls. Between-group pathways that correlated with mild cognitive impairments combined the central-executive and default-mode networks through the right insula. Apathy-correlated pathways of the posterior insula in participants with PD versus controls exhibited reduced right efferent and increased left afferent. Since apathy scores were comparable between the groups and effects of the other motor and non-motor symptoms were statistically removed by the analysis, the differences in apathy-correlated pathways were suggested as unique to PD. These pathways could be predictors in the pre-symptomatic phase in patients with apathy.

PMID:34449938 | DOI:10.1111/ejn.15432

Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept

Fri, 08/27/2021 - 18:00

Cereb Cortex. 2021 Aug 27:bhab273. doi: 10.1093/cercor/bhab273. Online ahead of print.


Functional connectivity (FC) is determined by similarity between functional magnetic resonance imaging (fMRI) signals from distinct brain regions. However, traditional FC analyses ignore temporal phase differences. Here, we addressed this limitation, using dynamic time warping (DTW) within a machine-learning framework, to study cortical FC patterns of 2 spatially adjacent but functionally distinct subcortical regions, namely Substantia Nigra Pars Compacta (SNc) and ventral tegmental area (VTA). We evaluate: 1) the influence of pair of brain regions considered, 2) the influence of warping window sizes, 3) the classification efficacy of DTW, and 4) the uniqueness of features identified. Whole brain 7 Tesla resting state fMRI scans from 81 healthy participants were used. FC between 2 subcortical regions of interests (ROIs) and 360 cortical parcels were computed using: 1) Pearson correlations (PCs), 2) dynamic time-warped PCs (DTW-PC). The separability of SNc-cortical and VTA-cortical network was validated on 40 participants and tested on the remaining 41, using a support vector machine (SVM). The SVM separated the SNc-cortical versus VTA-cortical network with 74.39 and 97.56% test accuracy using PC and DTW-PC, respectively. SVM-recursive feature elimination yielded 20 DTW-PC features that most strongly contributed to the separation of the networks and revealed novel VTA versus SNc preferential connections (P < 0.05, Bonferroni-Holm corrected).

PMID:34448816 | DOI:10.1093/cercor/bhab273

Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics

Fri, 08/27/2021 - 18:00

J Child Psychol Psychiatry. 2021 Aug 27. doi: 10.1111/jcpp.13512. Online ahead of print.


BACKGROUND: Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents.

METHODS: In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting-state fMRI data in early adolescence (baseline: aged 9-13 years). Across 250 brain regions, we assessed local graph theory-based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within-module z-score, and participation coefficient. Four years later (follow-up: ages 13-19 years), participants self-reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain-based variables that best explain the severity of SI symptoms at follow-up. Nested-cross-validation yielded model performance statistics for all LASSO models.

RESULTS: A combination of psychosocial and brain-based variables explained subsequent severity of SI (R2 = .55); the strongest was internalizing and externalizing symptom severity at follow-up. Follow-up LASSO regressions of psychosocial-only and brain-based-only variables indicated that psychosocial-only variables explained 55% of the variance in SI severity; in contrast, brain-based-only variables performed worse than the null model.

CONCLUSIONS: A linear combination of baseline and follow-up psychosocial variables best explained the severity of SI. Follow-up analyses indicated that graph theory resting-state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting-state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI.

PMID:34448494 | DOI:10.1111/jcpp.13512

Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

Fri, 08/27/2021 - 18:00

Diagnostics (Basel). 2021 Aug 5;11(8):1416. doi: 10.3390/diagnostics11081416.


Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

PMID:34441350 | DOI:10.3390/diagnostics11081416

Neuroimaging Studies of Nonsuicidal Self-Injury in Youth: A Systematic Review

Fri, 08/27/2021 - 18:00

Life (Basel). 2021 Jul 22;11(8):729. doi: 10.3390/life11080729.


Nonsuicidal self-injury (NSSI) is prevalent and affects mainly the youth population. It is prospectively associated with suicide attempts, making it a target for suicide prevention. Recently, several studies have investigated neural pathways of NSSI using neuroimaging. However, there is a lack of systematized appraisal of these findings. This systematic review aims to identify and summarize the main neuroimaging findings of NSSI in youth. We followed PRISMA statement guidelines and searched MEDLINE, APA PsycInfo, and Google Scholar databases for neuroimaging studies, irrespective of imaging modality, specifically investigating NSSI in samples with a mean age of up to 25 years old. Quality assessment was made using the Newcastle-Ottawa and Joanna Briggs Institute scales. The initial search retrieved 3030 articles; 21 met inclusion criteria, with a total of 938 subjects. Eighteen studies employed functional neuroimaging techniques such as resting-state and task-based fMRI (emotional, interpersonal exposure/social exclusion, pain, reward, and cognitive processing paradigms). Three studies reported on structural MRI. An association of NSSI behavior and altered emotional processing in cortico-limbic neurocircuitry was commonly reported. Additionally, alterations in potential circuits involving pain, reward, interpersonal, self-processing, and executive function control processes were identified. NSSI has complex and diverse neural underpinnings. Future longitudinal studies are needed to understand its developmental aspects better.

PMID:34440473 | DOI:10.3390/life11080729