Most recent paper

Deep Learning Model of fMRI Connectivity Predicts PTSD Symptom Trajectories in Recent Trauma Survivors

Mon, 06/07/2021 - 18:00

Neuroimage. 2021 Jun 4:118242. doi: 10.1016/j.neuroimage.2021.118242. Online ahead of print.

ABSTRACT

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

PMID:34098066 | DOI:10.1016/j.neuroimage.2021.118242

Psychosocial impairment following mild blast-induced traumatic brain injury in rats

Mon, 06/07/2021 - 18:00

Behav Brain Res. 2021 Jun 4:113405. doi: 10.1016/j.bbr.2021.113405. Online ahead of print.

ABSTRACT

Traumatic brain injury (TBI) is associated with increased risk for mental health disorders, impacting post-injury quality of life and societal reintegration. TBI is also associated with deficits in psychosocial processing, defined as the cognitive integration of social and emotional behaviors, however little is known about how these deficits manifest and their contributions to post-TBI mental health. In this pre-clinical investigation using rats, a single mild blast TBI (mbTBI) induced impairment of psychosocial processing in the absence of confounding physical polytrauma, post-injury motor deficits, affective abnormalities, or deficits in non-social behavior. Impairment severity correlated with acute upregulations of a known oxidative stress metabolite, 3-hydroxypropylmercapturic acid (3-HPMA), in urine. Resting state fMRI alterations in the acute post-injury period implicated key brain regions known to regulate psychosocial behavior, including orbitofrontal cortex (OFC), which is congruent with our previous report of elevated acrolein, a marker of neurotrauma, in this region following mbTBI. OFC of mbTBI-exposed rat demonstrated elevated mRNA expression of metabotropic glutamate receptors 1 and 5 (mGluR1/5) and injection of mGluR1/5-selective agonist in OFC of uninjured rat approximated mbTBI-induced psychosocial processing impairment, demonstrating a novel role for OFC in this psychosocial behavior. Furthermore, OFC may serve as a hotspot for TBI-induced disruption of psychosocial processing and subsequent mental health disorders.

PMID:34097900 | DOI:10.1016/j.bbr.2021.113405

Improving precision functional mapping routines with multi-echo fMRI

Mon, 06/07/2021 - 18:00

Curr Opin Behav Sci. 2021 Aug;40:113-119. doi: 10.1016/j.cobeha.2021.03.017. Epub 2021 Apr 22.

ABSTRACT

Rapidly developing approaches to acquiring and analyzing densely-sampled, single-subject fMRI data have opened new avenues for understanding the neurobiological basis of individual differences in behavior and could allow fMRI to become a more clinically useful tool. Here, we review briefly key insights from these precision functional mapping studies and a highlight significant barrier to their clinical translation. Specifically, that reliable delineation of functional brain networks in individual humans can require hours of resting-state fMRI data per-subject. We found recently that multi-echo fMRI improves the test-retest reliability of resting-state functional connectivity measurements, mitigating the need for acquiring large quantities of per -subject data. Because the benefits of multi-echo acquisitions are most pronounced in clinically important but artifact-prone brain regions, such as the subgenual cingulate and structures deep in the subcortex, this approach has the potential to increase the impact of precision functional mapping routines in both healthy and clinical populations.

PMID:34095359 | PMC:PMC8174781 | DOI:10.1016/j.cobeha.2021.03.017

Galvanic Vestibular Stimulation Improves Subnetwork Interactions in Parkinson's Disease

Mon, 06/07/2021 - 18:00

J Healthc Eng. 2021 May 13;2021:6632394. doi: 10.1155/2021/6632394. eCollection 2021.

ABSTRACT

BACKGROUND: Activating vestibular afferents via galvanic vestibular stimulation (GVS) has been recently shown to have a number of complex motor effects in Parkinson's disease (PD), but the basis of these improvements is unclear. The evaluation of network-level connectivity changes may provide us with greater insights into the mechanisms of GVS efficacy.

OBJECTIVE: To test the effects of different GVS stimuli on brain subnetwork interactions in both health control (HC) and PD groups using fMRI.

METHODS: FMRI data were collected for all participants at baseline (resting state) and under noisy, 1 Hz sinusoidal, and 70-200 Hz multisine GVS. All stimuli were given below sensory threshold, blinding subjects to stimulation. The subnetworks of 15 healthy controls and 27 PD subjects (on medication) were identified in their native space, and their subnetwork interactions were estimated by nonnegative canonical correlation analysis. We then determined if the inferred subnetwork interaction changes were affected by disease and stimulus type and if the stimulus-dependent GVS effects were influenced by demographic features.

RESULTS: At baseline, interactions with the visual-cerebellar network were significantly decreased in the PD group. Sinusoidal and multisine GVS improved (i.e., made values approaching those seen in HC) subnetwork interactions more effectively than noisy GVS stimuli overall. Worsening disease severity, apathy, depression, impaired cognitive function, and increasing age all limited the beneficial effects of GVS.

CONCLUSIONS: Vestibular stimulation has widespread system-level brain influences and can improve subnetwork interactions in PD in a stimulus-dependent manner, with the magnitude of such effects associating with demographics and disease status.

PMID:34094040 | PMC:PMC8137296 | DOI:10.1155/2021/6632394

Insular Connectivity Is Associated With Self-Appraisal of Cognitive Function After a Concussion

Mon, 06/07/2021 - 18:00

Front Neurol. 2021 May 21;12:653442. doi: 10.3389/fneur.2021.653442. eCollection 2021.

ABSTRACT

Concussion is associated with acute cognitive impairments, with declines in processing speed and reaction time being common. In the clinical setting, these issues are identified via symptom assessments and neurocognitive test (NCT) batteries. Practice guidelines recommend integrating both symptoms and NCTs into clinical decision-making, but correlations between these measures are often poor. This suggests that many patients experience difficulties in the self-appraisal of cognitive issues. It is presently unclear what neural mechanisms give rise to appraisal mismatch after a concussion. One promising target is the insula, which regulates aspects of cognition, particularly interoception and self-monitoring. The present study tested the hypothesis that appraisal mismatch is due to altered functional connectivity of the insula to frontal and midline structures, with hypo-connectivity leading to under-reporting of cognitive issues and hyper-connectivity leading to over-reporting. Data were collected from 59 acutely concussed individuals and 136 normative controls, including symptom assessments, NCTs and magnetic resonance imaging (MRI) data. Analysis of resting-state functional MRI supported the hypothesis, identifying insular networks that were associated with appraisal mismatch in concussed athletes that included frontal, sensorimotor, and cingulate connections. Subsequent analysis of diffusion tensor imaging also determined that symptom over-reporting was associated with reduced fractional anisotropy and increased mean diffusivity of posterior white matter. These findings provide new insights into the mechanisms of cognitive appraisal mismatch after a concussion. They are of particular interest given the central role of symptom assessments in the diagnosis and clinical management of concussion.

PMID:34093401 | PMC:PMC8175663 | DOI:10.3389/fneur.2021.653442

Altered Resting-State Brain Activity in Schizophrenia and Obsessive-Compulsive Disorder Compared With Non-psychiatric Controls: Commonalities and Distinctions Across Disorders

Mon, 06/07/2021 - 18:00

Front Psychiatry. 2021 May 21;12:681701. doi: 10.3389/fpsyt.2021.681701. eCollection 2021.

ABSTRACT

Backgrounds: Schizophrenia (SCZ) and obsessive-compulsive disorder (OCD) are classified as two chronic psychiatric disorders with high comorbidity rate and shared clinical symptoms. Abnormal spontaneous brain activity within the cortical-striatal neural circuits has been observed in both disorders. However, it is unclear if the common or distinct neural abnormalities underlie the neurobiological substrates in the resting state. Methods: Resting-state fMRI data were collected from 88 patients with SCZ, 58 patients with OCD, and 72 healthy control subjects. First, we examined differences in amplitude of low-frequency fluctuations (ALFF) among three groups. Resting-state functional connectivity (rsFC) analysis with the brain region that showed different ALFF as the seed was then conducted to identify the changes in brain networks. Finally, we examined the correlation between the altered activities and clinical symptoms. Results: Both the patients with SCZ and OCD showed increased ALFF in the right hippocampus and decreased ALFF in the left posterior cingulate cortex (PCC). SCZ patients exhibited increased ALFF in the left caudate [voxel-level family-wise error (FWE) P < 0.05] and decreased rsFC between the left caudate and right cerebellum, which correlated with positive symptoms. The left caudate showed increased rsFC with the right thalamus and bilateral supplementary motor complex (SMC) in OCD patients (cluster-level FWE P < 0.05). Conclusions: The hippocampus and PCC are common regions presenting abnormal local spontaneous neuronal activities in both SCZ and OCD, while the abnormality of the striatum can reflect the differences. Increased ALFF in the striatum and symptom-related weakened rsFC between the caudate and cerebellum showed SCZ specificity. Enhanced rsFC between the caudate and SMC may be a key characteristic in OCD. Our research shows the similarities and differences between the two diseases from the perspective of resting-state fMRI, which provides clues to understand the disease and find methods for treatment.

PMID:34093290 | PMC:PMC8176119 | DOI:10.3389/fpsyt.2021.681701

Revealing the Neural Mechanism Underlying the Effects of Acupuncture on Migraine: A Systematic Review

Mon, 06/07/2021 - 18:00

Front Neurosci. 2021 May 20;15:674852. doi: 10.3389/fnins.2021.674852. eCollection 2021.

ABSTRACT

Background: Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache and various neurological symptoms. Migraine is typically treated by pharmacological or non-pharmacological therapies to relieve pain or prevent migraine attacks. Pharmacological therapies show limited efficacy in relieving headache and are often accompanied by adverse effects, while the benefits of acupuncture, a non-pharmacological therapy, have been well-documented in both the treatment and prevention of acute migraine attacks. However, the underlying mechanism of the effect of acupuncture on relieving migraine remains unclear. Recent advances in neuroimaging technology have offered new opportunities to explore the underlying neural mechanism of acupuncture in treating migraine. To pave the way for future research, this review provides an overview neuroimaging studies on the use of acupuncture for migraine in the last 10 years. Methods: Using search terms about acupuncture, neuroimaging and migraine, we searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and China National Knowledge Infrastructure from January 2009 to June 2020 for neuroimaging studies that examined the effect of acupuncture in migraine. All published randomized and non-randomized controlled neuroimaging studies were included. We summarized the proposed neural mechanism underlying acupuncture analgesia in acute migraine, and the proposed neural mechanism underlying the sustained effect of acupuncture in migraine prophylaxis. Results: A total of 619 articles were retrieved. After removing reviews, meta-analyses, animal studies and etc., 15 articles were eligible and included in this review. The methods used were positron emission computed tomography (PET-CT; n = 2 studies), magnetic resonance spectroscopy (n = 1), and functional magnetic resonance imaging (fMRI; n = 12). The analyses used included the regional homogeneity (ReHo) method (n = 3), amplitude of low frequency (ALFF) method (n = 2), independent component analysis (ICA; n = 3), seed-based analysis (SBA; n = 1), both ICA and SBA (n = 1), Pearson's correlation to calculate functional connectivity (FC) between brain regions (n = 1), and a machine learning method (n = 1). Five studies focused on the instant effect of acupuncture, and the research objects were those with acute migraine (n = 2) and migraine in the interictal phase (n = 3). Ten studies focused on the lasting effect of acupuncture, and all the studies selected migraine patients in the interictal phase. This review included five task-based studies and 10 resting-state studies. None of the studies conducted a correlation analysis between functional brain changes and instant clinical efficacy. For studies that performed a correlation analysis between functional brain changes and sustained clinical efficacy, the prophylactic effect of acupuncture on migraine might be through regulation of the visual network, default mode network (DMN), sensory motor network, frontoparietal network (FPN), limbic system, and/or descending pain modulatory system (DPMS). Conclusion: The neural mechanism underlying the immediate effect of acupuncture analgesia remains unclear, and the neural mechanism of sustained acupuncture treatment for migraine might be related to the regulation of pain-related brain networks. The experimental design of neuroimaging studies that examined the effect of acupuncture in migraine also have some shortcomings, and it is necessary to standardize and optimize the experimental design. Multi-center neuroimaging studies are needed to provide a better insight into the neural mechanism underlying the effect of acupuncture on migraine. Multi-modality neuroimaging studies that integrate multiple data analysis methods are required for cross-validation of the neuroimaging results. In addition, applying machine learning methods in neuroimaging studies can help to predict acupuncture efficacy and screen for migraineurs for whom acupuncture treatment would be suitable.

PMID:34093119 | PMC:PMC8172773 | DOI:10.3389/fnins.2021.674852

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform

Mon, 06/07/2021 - 18:00

Front Neurosci. 2021 May 21;15:663403. doi: 10.3389/fnins.2021.663403. eCollection 2021.

ABSTRACT

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

PMID:34093115 | PMC:PMC8175789 | DOI:10.3389/fnins.2021.663403

Early-Stage Repetitive Transcranial Magnetic Stimulation Altered Posterior-Anterior Cerebrum Effective Connectivity in Methylazoxymethanol Acetate Rats

Mon, 06/07/2021 - 18:00

Front Neurosci. 2021 May 21;15:652715. doi: 10.3389/fnins.2021.652715. eCollection 2021.

ABSTRACT

The aim of the current resting-state functional magnetic resonance imaging (fMRI) study was to investigate the potential mechanism of schizophrenia through the posterior-anterior cerebrum imbalance in methylazoxymethanol acetate (MAM) rats and to evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) as an early-stage intervention. The rats were divided into four groups: the MAM-sham group, vehicle-sham group, MAM-rTMS group, and vehicle-rTMS group. The rTMS treatment was targeted in the visual cortex (VC) in adolescent rats. Granger Causality Analysis (GCA) was used to evaluate the effective connectivity between regions of interest. Results demonstrated a critical right VC-nucleus accumbens (Acb)-orbitofrontal cortex (OFC) pathway in MAM rats; significant differences of effective connectivity (EC) were found between MAM-sham and vehicle-sham groups (from Acb shell to OFC: t = -2.553, p = 0.021), MAM-rTMS and MAM-sham groups (from VC to Acb core: t = -2.206, p = 0.043; from Acb core to OFC: t = 4.861, p < 0.001; from Acb shell to OFC: t = 4.025, p = 0.001), and MAM-rTMS and vehicle-rTMS groups (from VC to Acb core: t = -2.482, p = 0.025; from VC to Acb shell: t = -2.872, p = 0.012; from Acb core to OFC: t = 4.066, p = 0.001; from Acb shell to OFC: t = 3.458, p = 0.004) in the right hemisphere. Results of the early-stage rTMS intervention revealed that right nucleus accumbens played the role as a central hub, and VC was a potentially novel rTMS target region during adolescent schizophrenia. Moreover, the EC of right nucleus accumbens shell and orbitofrontal cortex was demonstrated to be a potential biomarker. To our knowledge, this was the first resting-state fMRI study using GCA to assess the deficits of a visual-reward neural pathway and the effectiveness of rTMS treatment in MAM rats. More randomized controlled trials in both animal models and schizophrenia patients are needed to further elucidate the disease characteristics.

PMID:34093113 | PMC:PMC8176023 | DOI:10.3389/fnins.2021.652715

Neural correlates of posttraumatic anhedonia symptoms: Decreased functional connectivity between ventral pallidum and default mode network regions

Sat, 06/05/2021 - 18:00

J Psychiatr Res. 2021 May 27;140:30-34. doi: 10.1016/j.jpsychires.2021.05.061. Online ahead of print.

ABSTRACT

Anhedonia is common in individuals with traumatic experience. Anhedonia symptoms play an important role in posttraumatic psychopathology, and are related to various adverse outcomes. The current study is a preliminary neuroimaging study of the neural correlates of posttraumatic anhedonia symptoms. Resting-state fMRI data were acquired from 88 Chinese earthquake survivors. Whole brain analyses and exploratory ROI-to-ROI analyses were performed to examine the relationship between posttraumatic anhedonia symptoms and resting-state functional connectivity (rsFC) of reward-related subcortical nucleus including nucleus accumbens and ventral pallidum. The rsFC between left ventral pallidum and areas of bilateral posterior cingulate cortex (PCC) and precuneus cortex were found lower in the high posttraumatic anhedonia group, after controlling for sex, age and other posttraumatic stress symptoms. The rsFC between left ventral pallidum and PCC and the rsFC between left ventral pallidum and lateral parietal cortex were significantly lower in the high anhedonia group. Our findings suggest that decreased functional connectivity between the ventral pallidum and the brain default mode network (DMN) regions could be the neural correlates of posttraumatic anhedonia symptoms.

PMID:34090100 | DOI:10.1016/j.jpsychires.2021.05.061

Abnormal brain functional network dynamics in obsessive-compulsive disorder patients and their unaffected first-degree relatives

Sat, 06/05/2021 - 18:00

Hum Brain Mapp. 2021 Jun 5. doi: 10.1002/hbm.25555. Online ahead of print.

ABSTRACT

We utilized dynamic functional network connectivity (dFNC) analysis to compare participants with obsessive-compulsive disorder (OCD) with their unaffected first-degree relative (UFDR) and healthy controls (HC). Resting state fMRI was performed on 46 OCD, 24 UFDR, and 49 HCs, along with clinical assessments. dFNC analyses revealed two distinct connectivity states: a less frequent, integrated state characterized by the predominance of between-network connections (State I), and a more frequent, segregated state with strong within-network connections (State II). OCD patients spent more time in State II and less time in State I than HC, as measured by fractional windows and mean dwell time. Time in each state for the UFDR were intermediate between OCD patients and HC. Within the OCD group, fractional windows of time spent in State I was positively correlated with OCD symptoms (as measured by the obsessive compulsive inventory-revised [OCI-R], r = .343, p<.05, FDR correction) and time in State II was negatively correlated with symptoms (r = -.343, p<.05, FDR correction). Within each state we also examined connectivity within and between established intrinsic connectivity networks, and found that UFDR were similar to the OCD group in State I, but more similar to the HC groups in State II. The similarities between OCD and UFDR groups in temporal properties and State I connectivity indicate that these features may reflect the endophenotype for OCD. These results indicate that the temporal dynamics of functional connectivity could be a useful biomarker to identify those at risk.

PMID:34089285 | DOI:10.1002/hbm.25555

Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation

Sat, 06/05/2021 - 18:00

J Neurosci. 2021 May 20:JN-RM-2924-19. doi: 10.1523/JNEUROSCI.2924-19.2021. Online ahead of print.

ABSTRACT

As we learn to perform a motor task with novel dynamics, the central nervous system must adapt motor commands and modify sensorimotor transformations. The objective of the current research is to identify the neural mechanisms underlying the adaptive process. It has been shown previously that an increase in muscle co-contraction is frequently associated with the initial phase of adaptation and that co-contraction is gradually reduced as performance improves. Our investigation focused on the neural substrates of muscle co-contraction during the course of motor adaptation using a resting-state fMRI approach in healthy human subjects of both genders. We analyzed the functional connectivity in resting-state networks during three phases of adaptation, corresponding to different muscle co-contraction levels and found that change in the strength of functional connectivity in one brain network was correlated with a metric of co-contraction, and in another with a metric of motor learning. We identified the cerebellum as the key component for regulating muscle co-contraction, especially its connection to the inferior parietal lobule, which was particularly prominent in early stage adaptation. A neural link between cerebellum, superior frontal gyrus and motor cortical regions was associated with reduction of co-contraction during later stages of adaptation. We also found reliable changes in the functional connectivity of a network involving primary motor cortex, superior parietal lobule and cerebellum that were specifically related to the motor learning.SIGNIFICANCE STATEMENTIt is well known that co-contracting muscles is an effective strategy for providing postural stability by modulating mechanical impedance and thereby allowing the central nervous system to compensate for unfamiliar or unexpected physical conditions until motor commands can be appropriately adapted. The present study elucidates the neural substrates underlying the ability to modulate the mechanical impedance of a limb as we learn during motor adaptation. Using resting-state fMRI analysis we demonstrate that a distributed cerebellar-parietal-frontal network functions to regulate muscle co-contraction with the cerebellum as its key component.

PMID:34088798 | DOI:10.1523/JNEUROSCI.2924-19.2021