Resting-state neuroimaging studies: a new way of identifying differences and similarities among the anxiety disorders?
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ABSTRACT:
SUBMITTER: Peterson A
PROVIDER: S-EPMC4079145 | biostudies-literature | 2014 Jun
REPOSITORIES: biostudies-literature
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Project description:IntroductionFunctional Magnetic Resonance Imaging (fMRI) block-design experiments typically include active ON-blocks with presentation of cognitive tasks which are contrasted with OFF- blocks with no tasks presented. OFF-blocks in between ON-blocks can however, also be seen as a proxy for intermittent periods of resting, inducing temporary resting-states. We still do not know if brain activity during such intermittent periods reflects the same kind of resting-state activity as that obtained during a continuous period, as is typically the case in studies of the classic Default Mode Network (DMN). The purpose of the current study was therefore to investigate both similarities and differences in brain activity between intermittent and continuous resting conditions.MethodsThere were 47 healthy participants in the 3T fMRI experiment. Data for the intermittent resting-state condition were acquired from resting-periods in between active task-processing periods in a standard ON-OFF block design, with three different cognitive tasks presented during ON-blocks. Data for the continuous resting-state condition were acquired during a 5 min resting period after the task-design had been presented.Results and discussionThe results showed that activity was overall similar in the two conditions, but with some differences. These differences were within the DMN network, and for the interaction of DMN with other brain networks. DMN maps showed weak overlap between conditions in the medial prefrontal cortex (MPFC), and in particular for the intermittent compared to the continuous resting-state condition. Moreover, DMN showed strong connectivity with the salience network (SN) in the intermittent resting-state condition, particularly in the anterior insula and the supramarginal gyrus. The observed differences may reflect a "carry-over" effect from task-processing to the next resting-state period, not present in the continuous resting-state condition, causing interference from the ON-blocks. Further research is needed to fully understand the extent of differences between intermittent and continuous resting-state conditions.
| S-EPMC10435290 | biostudies-literature
Project description:A severe brain injury may lead to a disorder of consciousness (DOC) such as coma, vegetative state (VS), minimally conscious state (MCS) or locked-in syndrome (LIS). Till date, the diagnosis of DOC relies only on clinical evaluation or subjective scoring systems such as Glasgow coma scale, which fails to detect subtle changes and thereby results in diagnostic errors. The high rate of misdiagnosis and inability to predict the recovery of consciousness for DOC patients have created a huge research interest in the assessment of consciousness. Researchers have explored the use of various stimulation and neuroimaging techniques to improve the diagnosis. In this article, we present the important findings of resting-state as well as sensory stimulation methods and highlight the stimuli proven to be successful in the assessment of consciousness. Primarily, we review the literature based on (a) application/non-use of stimuli (i.e., sensory stimulation/resting state-based), (b) type of stimulation used (i.e., auditory, visual, tactile, olfactory, or mental-imagery), (c) electrophysiological signal used (EEG/ERP, fMRI, PET, EMG, SCL, or ECG). Among the sensory stimulation methods, auditory stimulation has been extensively used, since it is easier to conduct for these patients. Olfactory and tactile stimulation have been less explored and need further research. Emotionally charged stimuli such as subject's own name or narratives in a familiar voice or subject's own face/family pictures or music result in stronger responses than neutral stimuli. Studies based on resting state analysis have employed measures like complexity, power spectral features, entropy and functional connectivity patterns to distinguish between the VS and MCS patients. Resting-state EEG and fMRI are the state-of-the-art techniques and have a huge potential in predicting the recovery of coma patients. Further, EMG and mental-imagery based studies attempt to obtain volitional responses from the VS patients and thus could detect their command-following capability. This may provide an effective means to communicate with these patients. Recent studies have employed fMRI and PET to understand the brain-activation patterns corresponding to the mental imagery. This review promotes our knowledge about the techniques used for the diagnosis of patients with DOC and attempts to provide ideas for future research.
| S-EPMC7522478 | biostudies-literature
Project description:A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
| S-EPMC6333694 | biostudies-literature
Project description:Epilepsy is a disease characterized by abnormal spontaneous activity in the brain. Resting-state functional magnetic resonance imaging (RS-fMRI) is a powerful technique for exploring this activity. With good spatial and temporal resolution, RS-fMRI is a promising approach for accurate localization of the focus of seizure activity. Although simultaneous electroencephalogram-fMRI has been performed with patients in the resting state, most studies focused on activation. This mini-review focuses on RS-fMRI alone, including its computational methods and its application to epilepsy.
| S-EPMC5561896 | biostudies-other
Project description:IntroductionPersistent postural-perceptual dizziness (PPPD) (ICD-11) and anxiety disorders (ANX) share behavioural symptoms like anxiety, avoidance, social withdrawal, hyperarousal, or palpitation as well as neurological symptoms like vertigo, stance and gait disorders. Furthermore, previous studies have shown a bidirectional link between vestibulo-spatial and anxiety neural networks. So far, there have been no neuroimaging-studies comparing these groups.ObjectivesThe aim of this explorative study was to investigate differences and similarities of neural correlates between these two patient groups and to compare their findings with a healthy control group.Methods63 participants, divided in two patient groups (ANX = 20 and PPPD = 14) and two sex and age matched healthy control groups (HC-A = 16, HC-P = 13) were included. Anxiety and dizziness related pictures were shown during fMRI-measurements in a block-design in order to induce emotional responses. All subjects filled in questionnaires regarding vertigo (VSS, VHQ), anxiety (STAI), depression (BDI-II), alexithymia (TAS), and illness-perception (IPQ). After modelling the BOLD response with a standard canonical HRF, voxel-wise t-tests between conditions (emotional-negative vs neutral stimuli) were used to generate statistical contrast maps and identify relevant brain areas (pFDR < 0.05, cluster size >30 voxels). ROI-analyses were performed for amygdala, cingulate gyrus, hippocampus, inferior frontal gyrus, insula, supramarginal gyrus and thalamus (p ≤ 0.05).ResultsPatient groups differed from both HC groups regarding anxiety, dizziness, depression and alexithymia scores; ratings of the PPPD group and the ANX group did differ significantly only in the VSS subscale 'vertigo and related symptoms' (VSS-VER). The PPPD group showed increased neural responses in the vestibulo-spatial network, especially in the supramarginal gyrus (SMG), and superior temporal gyrus (STG), compared to ANX and HC-P group. The PPPD group showed increased neural responses compared to the HC-P group in the anxiety network including amygdala, insula, lentiform gyrus, hippocampus, inferior frontal gyrus (IFG) and brainstem. Neuronal responses were enhanced in visual structures, e.g. fusiform gyrus, middle occipital gyrus, and in the medial orbitofrontal cortex (mOFC) in healthy controls compared to patients with ANX and PPPD, and in the ANX group compared to the PPPD group.ConclusionsThese findings indicate that neuronal responses to emotional information in the PPPD and the ANX group are comparable in anxiety networks but not in vestibulo-spatial networks. Patients with PPPD revealed a stronger neuronal response especially in SMG and STG compared to the ANX and the HC group. These results might suggest higher sensitivity and poorer adaptation processes in the PPPD group to anxiety and dizziness related pictures. Stronger activation in visual processing areas in HC subjects might be due to less emotional and more visual processing strategies.
| S-EPMC9879992 | biostudies-literature
Project description:Depression, anxiety and apathy are common mood disturbances in Parkinson's disease (PD) but their pathophysiology is unclear. Advanced neuroimaging has been increasingly used to unravel neural substrates linked to these disturbances. A systematic review is provided of neuroimaging findings in depression, anxiety and apathy in PD. A PubMed, MEDLINE and EMBASE search of peer-reviewed original research articles on these mood disturbances in PD identified 38 studies on depression, eight on anxiety and 14 on apathy in PD. Most of the imaging studies used either position emission tomography or single-photon emission computed tomography techniques. These studies generally suggest increased neural activity in the prefrontal regions and decreased functional connectivity between the prefrontal-limbic networks in depressed patients. Functional imaging studies revealed an inverse correlation between dopaminergic density in the caudate and putamen with the severity of anxiety in PD. There was no consistent correlation between dopaminergic density of thalamus and anxiety. Studies demonstrated both positive and inverse correlations between apathy and metabolism or activity in the striatum, amygdalar, prefrontal, temporal and parietal regions. The clinical variability of study subjects and differences in image pre-processing and analytical strategies may contribute to discrepant findings in these studies. Both nigrostriatal and extra-nigrostriatal pathways (in particular the frontal region and its connecting areas) are affected in mood disorders in PD. Identifying the relative contributions of these neural pathways in PD patients with overlapping motor and mood symptoms could provide new pathophysiological clues for the development of better therapeutic targets for affected patients.
| S-EPMC5084819 | biostudies-other
Project description:Numerous functional magnetic resonance imaging (fMRI) studies have reported sex differences. To empirically evaluate for evidence of excessive significance bias in this literature, we searched for published fMRI studies of human brain to evaluate sex differences, regardless of the topic investigated, in Medline and Scopus over 10 years. We analyzed the prevalence of conclusions in favor of sex differences and the correlation between study sample sizes and number of significant foci identified. In the absence of bias, larger studies (better powered) should identify a larger number of significant foci. Across 179 papers, median sample size was n?=?32 (interquartile range 23-47.5). A median of 5 foci related to sex differences were reported (interquartile range, 2-9.5). Few articles (n?=?2) had titles focused on no differences or on similarities (n?=?3) between sexes. Overall, 158 papers (88%) reached "positive" conclusions in their abstract and presented some foci related to sex differences. There was no statistically significant relationship between sample size and the number of foci (-0.048% increase for every 10 participants, p?=?0.63). The extremely high prevalence of "positive" results and the lack of the expected relationship between sample size and the number of discovered foci reflect probable reporting bias and excess significance bias in this literature.
| S-EPMC5904173 | biostudies-literature
Project description:Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.
| S-EPMC3625191 | biostudies-literature
Project description:BackgroundMotor functional neurological disorder (mFND) is a clinical diagnosis with reliable features; however, patients are reluctant to accept the diagnosis and physicians themselves bear doubts on potential misdiagnoses. The identification of a positive biomarker could help limiting unnecessary costs of multiple referrals and investigations, thus promoting early diagnosis and allowing early engagement in appropriate therapy.ObjectivesTo test whether resting-state (RS) functional magnetic resonance imaging could discriminate patients suffering from mFND from healthy controls.MethodsWe classified 23 mFND patients and 25 age- and gender-matched healthy controls based on whole-brain RS functional connectivity (FC) data, using a support vector machine classifier and the standard Automated Anatomic Labeling (AAL) atlas, as well as two additional atlases for validation.ResultsAccuracy, specificity and sensitivity were over 68% (p = 0.004) to discriminate between mFND patients and controls, with consistent findings between the three tested atlases. The most discriminative connections comprised the right caudate, amygdala, prefrontal and sensorimotor regions. Post-hoc seed connectivity analyses showed that these regions were hyperconnected in patients compared to controls.ConclusionsThe good accuracy to discriminate patients from controls suggests that RS FC could be used as a biomarker with high diagnostic value in future clinical practice to identify mFND patients at the individual level.
| S-EPMC5651543 | biostudies-literature
Project description:There exists growing interest in understanding the dynamics of resting state functional magnetic resonance imaging (rs-fMRI) to establish mechanistic links between individual patterns of spontaneous neural activation and corresponding behavioral measures in both normative and clinical populations. Here we propose and validate a novel approach in which whole-brain rs-fMRI data are mapped to a specific low-dimensional representation-affective valence and arousal processing-prior to dynamic analysis. This mapping process constrains the state space such that both independent validation and visualization of the system's dynamics become tractable. To test this approach, we constructed neural decoding models of affective valence and arousal processing from brain states induced by International Affective Picture Set image stimuli during task-related fMRI in (n = 97) healthy control subjects. We applied these models to decode moment-to-moment affect processing in out-of-sample subjects' rs-fMRI data and computed first and second temporal derivatives of the resultant valence and arousal time-series. Finally, we fit a second set of neural decoding models to these derivatives, which function as neurally constrained ordinary differential equations (ODE) underlying affect processing dynamics. To validate these decodings, we simulated affect processing by numerical integration of the true temporal sequence of neurally decoded derivatives for each subject and demonstrated that these decodings generate significantly less (p < 0.05) group-level simulation error than integration based upon decoded derivatives sampled uniformly randomly from the true temporal sequence. Indeed, simulations of valence and arousal processing were significant for up to four steps of closed-loop simulation (Δt = 2.0 s) for both valence and arousal, respectively. Moreover, neural encoding representations of the ODE decodings include significant clusters of activation within brain regions associated with affective reactivity and regulation. Our work has methodological implications for efforts to identify unique and actionable biomarkers of possible future or current psychopathology, particularly those related to mood and emotional instability.
| S-EPMC10406310 | biostudies-literature