Article

AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages

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Abstract

A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.

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... Then we perform a voxel-wise two-sided test with Mann-Whitney U-Test and compute Rank Bisseral Correlation (RBC) as an effect size. After selecting the voxels with a p ∈ [0., 0.025] ∪ [0.975, 1.], using 3dClusterize from AFNI [96], we find clusters with at least 200 voxels. Then we apply whereami from AFNI [96] to match those clusters with the ROIs defined in the template that is used in the Neuromark pipeline [97]. ...
... After selecting the voxels with a p ∈ [0., 0.025] ∪ [0.975, 1.], using 3dClusterize from AFNI [96], we find clusters with at least 200 voxels. Then we apply whereami from AFNI [96] to match those clusters with the ROIs defined in the template that is used in the Neuromark pipeline [97]. We call this template the Neuromark atlas in the rest of the text. ...
... To create the Neuromark atlas, the spatial ICA components of the Neuromark template have been combined to create an atlas by simple overlapping. Then atlas has been added to AFNI [96] environment. ...
Preprint
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameterized models.However, a single modality provides only a limited view of the highly complex brain. Critically, supervised models in clinical settings lack accurate diagnostic labels for training. Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings. This work presents a novel multi-scale coordinated framework for learning multiple representations from multimodal neuroimaging data. We propose a general taxonomy of informative inductive biases to capture unique and joint information in multimodal self-supervised fusion. The taxonomy forms a family of decoder-free models with reduced computational complexity and a propensity to capture multi-scale relationships between local and global representations of the multimodal inputs. We conduct a comprehensive evaluation of the taxonomy using functional and structural magnetic resonance imaging (MRI) data across a spectrum of Alzheimer's disease phenotypes and show that self-supervised models reveal disorder-relevant brain regions and multimodal links without access to the labels during pre-training. The proposed multimodal self-supervised learning yields representations with improved classification performance for both modalities. The concomitant rich and flexible unsupervised deep learning framework captures complex multimodal relationships and provides predictive performance that meets or exceeds that of a more narrow supervised classification analysis. We present elaborate quantitative evidence of how this framework can significantly advance our search for missing links in complex brain disorders.
... Prewhitening methods are implemented in the major software packages AFNI (Cox 1996), FSL (Jenkinson et al. 2012) and SPM (Penny et al. 2011). Yet, many of these standard prewhitening techniques have received criticism for failing to effectively remove residual autocorrelation (Worsley et al. 2002, Eklund et al. 2012). ...
... This suggests that for volumetric fMRI analysis where the computational burden associated with local prewhitening may be substantial, lower-order AR models may be worth consideration. This echoes the relatively strong performance of AFNI (Cox 1996), which assumes a spatially varying ARMA(1,1) model with no smoothing, observed by Olszowy et al. (2019). ...
Preprint
In task fMRI analysis, OLS is typically used to estimate task-induced activation in the brain. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform prewhitening prior to OLS to satisfy the assumption of residual independence, equivalent to GLS. While theoretically straightforward, a major challenge in prewhitening in fMRI is accurately estimating the residual autocorrelation at each location of the brain. Assuming a global autocorrelation model, as in several fMRI software programs, may under- or over-whiten particular regions and fail to achieve nominal false positive control across the brain. Faster multiband acquisitions require more sophisticated models to capture autocorrelation, making prewhitening more difficult. These issues are becoming more critical now because of a trend towards subject-level analysis, where prewhitening has a greater impact than in group-average analyses. In this article, we first thoroughly examine the sources of residual autocorrelation in multiband task fMRI. We find that residual autocorrelation varies spatially throughout the cortex and is affected by the task, the acquisition method, modeling choices, and individual differences. Second, we evaluate the ability of different AR-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We find that allowing the prewhitening filter to vary spatially is the most important factor for successful prewhitening, even more so than increasing AR model order. To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation based on parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.
... For group analyses and visualization, anatomical MRIs were spatially normalized in order to determine the TAL coordinates of VOTC intracerebral contacts. The cortical surface used to display group contact locations and maps was obtained from segmenting the Collin27 brain from AFNI (Cox, 1996), which is aligned to the TAL space. We used TAL transformed coordinates to compute maps of the local proportion of face-selective intracerebral contacts across the VOTC. ...
Article
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In vivo intracranial recordings of neural activity offer a unique opportunity to understand human brain function. Intracranial electrophysiological (iEEG) activity related to sensory, cognitive or motor events manifests mostly in two types of signals: event-related local field potentials in lower frequency bands (<30 Hz, LF) and broadband activity in the higher end of the frequency spectrum (>30 Hz, High frequency, HF). While most current studies rely exclusively on HF, thought to be more focal and closely related to spiking activity, the relationship between HF and LF signals is unclear, especially in human associative cortex. Here, we provide a large-scale in-depth investigation of the spatial and functional relationship between these 2 signals based on intracranial recordings from 121 individual brains (8000 recording sites). We measure category-selective responses to complex ecologically salient visual stimuli – human faces – across a wide cortical territory in the ventral occipito-temporal cortex (VOTC), with a frequency-tagging method providing high signal-to-noise ratio (SNR) and the same objective quantification of signal and noise for the two frequency ranges. While LF face-selective activity has higher SNR across the VOTC, leading to a larger number of significant electrode contacts especially in the anterior temporal lobe, LF and HF display highly similar spatial, functional, and timing properties. Specifically, and contrary to a widespread assumption, our results point to nearly identical spatial distribution and local spatial extent of LF and HF activity at equal SNR. These observations go a long way towards clarifying the relationship between the two main iEEG signals and reestablish the informative value of LF iEEG to understand human brain function.
... A custom 5-channel receive coil was used, which was rigidly fixed to the head implant 43 Image preprocessing. For squirrels, rats, and marmosets, data were similarly processed with custom preprocessing pipelines using the Analysis of Functional Neu-roImages (AFNI) 44 and FMRIB Software Library (FSL) 45 software packages. Raw functional images were converted to NifTI format using dcm2niix 46 and reoriented from the sphinx position using FSL. ...
Article
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Robust frontoparietal connectivity is a defining feature of primate cortical organization. Whether mammals outside the primate order, such as rodents, possess similar frontoparietal functional connectivity organization is a controversial topic. Previous work has primarily focused on comparing mice and rats to primates. However, as these rodents are nocturnal and terrestrial, they rely much less on visual input than primates. Here, we investigated the functional cortical organization of grey squirrels which are diurnal and arboreal, thereby better resembling primate ecology. We used ultra-high field resting-state fMRI data to compute and compare the functional connectivity patterns of frontal regions in grey squirrels (Sciurus carolinensis), rats (Rattus norvegicus), and marmosets (Callithrix jacchus). We utilized a fingerprinting analysis to compare interareal patterns of functional connectivity from seeds across frontal cortex in all three species. The results show that grey squirrels, but not rats, possess a frontoparietal connectivity organization that resembles the connectivity pattern of marmoset lateral prefrontal cortical areas. Since grey squirrels and marmosets have acquired an arboreal way of life but show no common arboreal ancestor, the expansion of the visual system and the formation of a frontoparietal connectivity architecture might reflect convergent evolution driven by similar ecological niches in primates and tree squirrels. Comparisons of frontoparietal connectivity between marmosets, rats and grey squirrels suggest the formation of a common frontoparietal network architecture among arboreal species (grey squirrels and marmosets) that might reflect convergent evolution.
... This radius length corresponds to a recommended smoothing kernel for preserving regional specificity and sensitivity in amygdala analyses (Morawetz et al., 2008). Next, we mapped the spheres onto the amygdala mask from the Brainnetome Atlas in AFNI 21.0 (Fan et al., 2016;Cox, 1996). The atlas subdivides the amygdala into medial and lateral portions, the latter of which approximates the basolateral subnucleus. ...
Article
The amygdala is a key component in predominant neural circuitry models of psychopathy. Yet, after two decades of neuroimaging research on psychopathy, the reproducibility of amygdala findings is questionable. We systematically reviewed MRI studies (81 of adults, 53 of juveniles) to determine the consistency of amygdala findings across studies, as well as within specific types of experimental tasks, community versus forensic populations, and the lowest- versus highest-powered studies. Three primary findings emerged. First, the majority of studies found null relationships between psychopathy and amygdala structure and function, even in the context of theoretically relevant tasks. Second, findings of reduced amygdala activity were more common in studies with low compared to high statistical power. Third, the majority of peak coordinates of reduced amygdala activity did not fall primarily within the anatomical bounds of the amygdala. Collectively, these findings demonstrate significant gaps in the empirical support for the theorized role of the amygdala in psychopathy and indicate the need for novel research perspectives and approaches in this field.
... Then, we unified the brain orientations , removed the shading artifacts and stripped the skull through Analysis of Functiona l NeuroImages (AFNI) software. 15 We registered the skull-stripped brain using FMRIB Software Library (FSL) software 16 onto the Montreal Neurological Institute (MNI) 152 standard brain template 17 with a spatial resolution of 1x1x1 mm 3 to regulate the brain structure differences among the participants ( Figure 1A). Finally, we cropped the backgrounds without loss of any potentially informative voxels. ...
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Objective: To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction. Methods: T2-weighted oblique coronal images of the brain MRI epilepsy protocol performed in patients were used. The training set included 320 participants with 160 no, 100 left and 60 right hippocampal sclerosis, and cross-validation was implemented. The test set consisted of 302 participants with 252 no, 25 left and 25 right hippocampal sclerosis. Since the test set was imbalanced, we took an average of the accuracy achieved within each group to measure a balanced accuracy for multiclass and binary classifications. The dataset was composed to include not only healthy participants but also participants with abnormalities besides hippocampal sclerosis in the control group. We visualized the reasons for the model prediction using the layer-wise relevance propagation method. Results: When evaluated on the validation of the training set, we achieved multiclass and binary classification accuracy of 87.5% and 88.8% from the voting ensemble of six models. Evaluated on the test sets, we achieved multiclass and binary classification accuracy of 91.5% and 89.76%. The distinctly sparse visual interpretations were provided for each individual participant and group to suggest the contribution of each input voxel to the prediction on the MRI. Significance: The current interpretable deep learning-based model is promising for adapting effectively to clinical settings by utilizing commonly used data, such as MRI, with realistic abnormalities faced by neurologists to support the diagnosis of hippocampal sclerosis with plausible visual interpretation.
... Following the conversion of the original DICOM images to NIFTI format, we used AFNI (Cox, 1996) to preprocess MRI data. Preprocessing included the following steps: despiking, head motion correction, affine alignment with subject-specific anatomical (T1-weighted) image, nonlinear alignment to a group MNI template (MNI152_2009), combination of data across the three echoes using AFNI's "optimally combine" method, and smoothing with an isotropic full-width half-maximum of 4 mm. ...
Preprint
Event segmentation is a spontaneous part of perception, important for processing continuous information and organizing it into memory. While neural and behavioral event segmentation show a degree of inter-subject consistency, meaningful individual variability exists atop these shared patterns. Here we characterized individual differences in the location of neural and behavioral event boundaries across four short movies that varied in both high- and low-level features and evoked variable interpretations. We studied the same individuals across movies to investigate the extent to which individual segmentation styles are stable irrespective of the stimulus (i.e., "trait-like") versus content-dependent. Results showed that across-subject event boundary alignment follows a posterior-to-anterior gradient that is tightly correlated with the rate of segmentation; slower segmenting regions that integrate information over longer time periods show more individual variability. We found that certain aspects of movie content-namely, continuity editing and social content-drive more shared boundaries and are better suited to pull out individual differences. We identified a subset of regions, specific to each movie, in which neural boundary locations are both aligned with behavioral boundaries during encoding and predictive of stimulus interpretation, suggesting that event segmentation may be a mechanism by which narratives generate variable memories and appraisals of stimuli.
... AFNI (Cox, 1996;Cox & Hyde, 1997) was used to rescale, smooth (4 mm FWHM), and mask data. First-level GLMs (3dDeconvolve) were constructed with eight regressors with boxcars of fixed (GAM) or variable duration (dmBLOCK) convolved with a gamma hemodynamic response function: ...
Article
Background: Depression risk increases during adolescent development, and individual differences in neural sensitivity to peer feedback (rejection vs. acceptance) may be a key diathesis in understanding stress-related depression risk. Methods: At baseline, adolescents (12-14 years old; N = 124) completed clinical interviews and self-report symptom measures, and the Chatroom Task while MRI data were acquired. The majority of participants provided usable MRI data (N = 90; 76% female), which included adolescents with no maternal depression history (low risk n = 64) and those with a maternal depression history (high risk n = 26). Whole-brain regression models probed group differences in neural sensitivity following peer feedback, and whole-brain linear mixed-effects models examined neural sensitivity to peer feedback by peer stress interactions relating to depression symptoms at up to nine longitudinal assessments over 2 years. Results: Whole-brain cluster-corrected results indicated brain activation moderating the strong positive association between peer interpersonal stress and depression over time. This included activation in the anterior insula, cingulate, amygdala, and striatum during anticipation and receipt of feedback (i.e., rejection vs. acceptance). Moderation effects were stronger when examining peer interpersonal (vs. non-interpersonal) stress and in relation to depression (vs. social anxiety) symptoms. Conclusions: Neural responses to peer feedback in key social and incentive processing brain regions may reflect core dispositional risk factors that interact with peer interpersonal stressors to predict adolescent depression symptom severity over time.
... The AFNI package (20) was used to calculate voxel/vertex-wise weighted degree centrality as a measure of GloCon with sparsity threshold of 0.1 (21). A combined parameter labelled LocCon was calculated as In the last step, the processed outputs (GloCon, LocCon, VTA-Con) were parcellated using a combination of HCP-derived cortical parcellation consisting of 180 parcels per hemisphere (23) and resting-state networkbased sub-segmentation of Freesurfer-derived subcortical grey matter structures (in total 68 subcortical subsegments). ...
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Background Deep brain stimulation of the internal globus pallidus (GPi DBS) is an invasive therapeutic modality intended to retune abnormal central nervous system patterns and relieve the patient of dystonic or other motor symptoms. Objectives The aim of the presented research was to determine the neuroanatomical signature of GPi DBS modulation and its association with the clinical outcome. Methods This open-label fixed-order study with cross-sectional validation against healthy controls analysed the resting-state functional MRI activity changes induced by GPi DBS in 18 dystonia patients of heterogeneous aetiology, focusing on both global (full brain) and local connectivity (local signal homogeneity). Results Compared to the switched-off state, the activation of GPi DBS led to the restoration of global subcortical connectivity patterns (in both putamina, diencephalon and brainstem) towards those of healthy controls, with positive direct correlation over large-scale cortico-basal ganglia-thalamo-cortical and cerebellar networks with the clinical improvement. Nonetheless, on average, GPi DBS also seemed to bring local connectivity both in the cortical and subcortical regions farther away from the state detected in healthy controls. Interestingly, its correlation with clinical outcome showed that in better DBS responders, local connectivity defied this effect and approached healthy controls. Conclusions All in all, the extent of restoration of both these main metrics of interest towards the levels found in healthy controls clearly correlated with the clinical improvement, indicating that the restoration of network state towards more physiological condition may be a precondition for successful GPi DBS outcome in dystonia.
... The analytical methods for both approaches have been detailed in our previous work (Caparelli et al., 2022), and summarized below. All analyses were carried out using FSL (the FMRIB Software Library, Oxford, UK) (Jenkinson et al., 2012), AFNI (Cox, 1996), or RStudio (RStudio Team, 2020). ...
Article
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The positive treatment outcomes of low frequency (LF) repetitive transcranial magnetic stimulation (rTMS) when applied over the right dorsolateral prefrontal cortex (DLPFC) in treatment-refractory depression has been verified. However, the mechanism of action behind these results have not been well-explored. In this work we used simultaneous functional magnetic resonance imaging (fMRI) during TMS to explore the effect of LF rTMS on brain activity when applied to the right [RDLPFC1 (MNI: 50, 30, 36)] and left DLPFC sites [LDLPFC1 (MNI: -50, 30, 36), LDLPFC2 (MNI: -41, 16, 54)]. Seventeen healthy adult volunteers participated in this study. To identify brain areas affected by rTMS, an independent component analysis and a general linear model were used. Our results showed an important laterality effect when contrasting rTMS over the left and right sites. Specifically, LF rTMS increased brain activity at the striatum, thalamus, and areas of the default mode network when applied to the right, but not to the contralateral left DLPFC. In contrast, no site differences were observed when evaluating the effect of LF rTMS over the two left sites. These findings demonstrate that LF rTMS to the right DLPFC was able to stimulate the cortico-striato-thalamo-cortical pathway, which is dysregulated in patients with major depressive disorder; therefore, possibly providing some neurobiological justification for the successful outcomes found thus far for LF rTMS in the treatment of depression.
... g pipeline (version 3.22.0; Glasser et al., 2013), which includes gradient nonlinearity correction via gradunwarp. Both T1 and T2 data were used to generate white matter and pial surface models using FreeSurfer (version 5.3.0;Fischl, 2012). We then removed non-brain regions from the T1 data using the 3dSkullStrip command from AFNI (version 18.2.04;Cox, 1996), followed by correcting for inhomogeneities in the intensity profile of gray matter and white matter voxels using AFNI's 3dUnifize. ...
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Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.
... These include geodesic distance matrices (-GD) mapped to multiple parcellation schemes as well as vertex-wise cortical thickness and curvature data (-Morphology). The structural workflow includes tools from AFNI (Cox, 1996), FSL (Jenkinson et al., 2012), ANTs (Avants et al., 2011), Mrtrix3 (Tournier et al., 2019) and FreeSurfer (Fischl, 2012). Further information about the usage and outputs is found in the structural processing section in the online documentation. ...
Article
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Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.
... fMRI data were preprocessed with AFNI software (http://afni.nimh. nih.gov/afni) (Cox, 1996;Cox and Hyde, 1997;Taylor et al., 2018). The structural and functional reference images were co-registered . ...
Article
State-dependent reallocation of cognitive resources is impaired in schizophrenia and may be underlined by alterations in brain local-connectivity. Increasing evidence suggests local connectivity reductions from rest to task in healthy individuals, while insufficient information is available for schizophrenia spectrum. Resting-state and stop-signal task fMRI scans of 107 healthy controls and 32 patients with DSM-IV-TR schizophrenia or schizoaffective disorder were analyzed. As primary aim we measured within-group shifts in local-connectivity from rest to task as voxel-wise Regional Homogeneity (ReHo-shift). Secondary aims were to test: i) Between-groups differences in ReHo-rest, ReHo-task and ReHo-shift; ii) ReHo covariations with task performance (=shorter reaction times) and severity of symptoms (SAPS/SANS scores). Age, sex, and education were accounted for as covariates. Motion, global-signal-regression, antipsychotic dosage and smoothing associations with ReHo were evaluated. Rest-to-task ReHo reductions occurred in both groups on a whole-brain level (False-Discovery-Rate p=0.05). Trends of greater ReHo reductions in patients versus controls were observed. Controls performed better than patients (p<0.001). ReHo negatively correlated with performance in both groups. ReHo-shift predicted worse performance in controls, but better performance in patients (uncorrected p=0.05). ReHo reductions correlated with severity of symptoms. State-dependent reconfigurations in local-connectivity provide new links between neurobiology and behavioral/clinical features of the schizophrenia spectrum.
... We investigated this using both task and resting state functional connectivity (FC) analyses. For the threat-safety reversal task we used the generalised psychophysiological interaction (gPPI) framework (McLaren et al., 2012) implemented via AFNI (Cox, 1996) and Nilearn (Abraham et al., 2014) to estimate FC (Supplementary information). After calculating FC matrices for each condition, the same preceding threat reversal and safety reversal contrasts were conducted (see Threat-safety reversal task section). ...
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Background: Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in individuals with OCD. Methods: A clinical sample of individuals with OCD (N=45) and matched healthy controls (N=45) underwent functional Magnetic Resonance Imaging (fMRI). While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. Results: We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task (r = 0.39, pFWE = 0.001). Conclusions: These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.
... The BOLD activation maps were created using the 3dDeconvolve general linear model function with the given boxcar design of the stimulation paradigm by AFNI 56 . The normalized BOLD responses were determined by the evoked BOLD signals over the injured regions of interests (ROIs) on the left hemisphere then dividing the BOLD signals over the equivalent non-injured (contralateral) ROIs on the right hemisphere. ...
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Traumatic brain injury causes inflammation and glial scarring that impede brain tissue repair, so stimulating angiogenesis and recovery of brain function remain challenging. Here we present an adaptable conductive microporous hydrogel consisting of gold nanoyarn balls-coated injectable building blocks possessing interconnected pores to improve angiogenesis and recovery of brain function in traumatic brain injury. We show that following minimally invasive implantation, the adaptable hydrogel is able to fill defects with complex shapes and regulate the traumatic brain injury environment in a mouse model. We find that placement of this injectable hydrogel at peri-trauma regions enhances mature brain-derived neurotrophic factor by 180% and improves angiogenesis by 250% in vivo within 2 weeks after electromagnetized stimulation, and that these effects facilitate neuron survival and motor function recovery by 50%. We use blood oxygenation level-dependent functional neuroimaging to reveal the successful restoration of functional brain connectivity in the corticostriatal and corticolimbic circuits. Traumatic brain injury can cause long-term disability and thus constitutes a substantial healthcare burden worldwide. Here, the authors report a conductive microporous hydrogel to improve angiogenesis and recovery of brain function in traumatic brain lesions.
... As reported in Schintu et al. (2020b), functional and structural MRI data were preprocessed using the AFNI software package (Cox, 1996) and following a general preprocessing approach (Wang et al., 2014). The anatomical scans were segmented using Freesurfer (Dale et al., 1999). ...
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Prism adaptation (PA) is a form of visuomotor training that produces both sensorimotor and cognitive aftereffects depending on the direction of the visual displacement. Recently, a neural framework explaining both types of PA-induced aftereffects has been proposed, but direct evidence for it is lacking. We employed Structural Equation Modeling (SEM), a form of effective connectivity analysis, to establish directionality among connected nodes of the brain network thought to subserve PA. The findings reveal two distinct network branches: (1) a loop involving connections from the parietal cortices to the right parahippocampal gyrus, and (2) a branch linking the lateral premotor cortex to the parahippocampal gyrus via the cerebellum. Like the sensorimotor aftereffects, the first branch exhibited qualitatively different modulations for left versus right PA, and critically, changes in these connections were correlated with the magnitude of the sensorimotor aftereffects. Like the cognitive aftereffects, changes in the second branch were qualitatively similar for left and right PA, with greater change for left PA and a trend correlation with cognitive aftereffects. These results provide direct evidence that PA is supported by two functionally distinct subnetworks, a parietal-temporal network responsible for sensorimotor aftereffects and a fronto-cerebellar network responsible for cognitive aftereffects.
... We processed and analyzed all structural and functional imaging data using Analysis of Functional NeuroImages (AFNI) software. 47 We discarded the first 3 volumes and performed slice timing correction for each remaining volume. The anatomic image was aligned to an echo-planar image and warped to the MNI152_T1_2009c T 1 -weighted anatomic template. ...
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Background: We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach–avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC. Methods: We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures. Results: Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = −0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = −0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = −0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27). Limitations: Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference. Conclusion: Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.
... We acquired functional images using the following EPI sequence parameters: TR/TE/ FA = 2000 ms/30 ms/90˚, FOV = 240 x 240 mm, matrix = 80 x 80, 37 oblique slices, ascending sequential slice acquisition, slice thickness = 2.5 mm with 0.5 mm gap, final resolution 3.0 x 3.0 x 3.0 mm 3 . We performed all MRI preprocessing using AFNI [50] (Version AFNI_19.1.04) unless otherwise noted. ...
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In this study, we merged methods from engineering control theory, machine learning, and human neuroimaging to critically test the putative role of the dorsal anterior cingulate cortex (dACC) in goal-directed performance monitoring during an emotion regulation task. Healthy adult participants (n = 94) underwent cued-recall and re-experiencing of their responses to affective image stimuli with concurrent functional magnetic resonance imaging and psychophysiological response recording. During cued-recall/re-experiencing trials, participants engaged in explicit self-regulation of their momentary affective state to match a pre-defined affective goal state. Within these trials, neural decoding methods measured affect processing from fMRI BOLD signals across the orthogonal affective dimensions of valence and arousal. Participants’ affective brain states were independently validated via facial electromyography (valence) and electrodermal activity (arousal) responses. The decoded affective states were then used to contrast four computational models of performance monitoring (i.e., error, predicted response outcome, action-value, and conflict) by their relative abilities to explain emotion regulation task-related dACC activation. We found that the dACC most plausibly encodes action-value for both valence and arousal processing. We also confirmed that dACC activation directly encodes affective arousal and also likely encodes recruitment of attention and regulation resources. Beyond its contribution to improving our understanding of the roles that the dACC plays in emotion regulation, this study introduced a novel analytical framework through which affect processing and regulation may be functionally dissociated, thereby permitting mechanistic analysis of real-world emotion regulation strategies, e.g., distraction and reappraisal, which are widely employed in cognitive behavioral therapy to address clinical deficits in emotion regulation.
... Edge detection identifies key features in an image, which, unless otherwise stated, means a 3D volume here and below. Popular neuroimaging toolboxes such as AFNI (Cox, 1996) and FSL (Smith et al., 2004) typically include bespoke tools for edge detection, where they are commonly used to generate an outline of an image or of specific features within the image. For example, locations of contrast showing tissue boundary delineation. ...
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Detecting and visualizing edges is important in several neuroimaging and medical imaging applications. For example, it is common to use edge maps to ensure the automatic alignment of low-resolution functional MRI images to match a high-resolution structural image has been successful. Specifically, software toolboxes like FSL and AFNI generate volumetric edge maps that can be particularly useful for visually assessing the alignment of datasets, overlaying the edge map of one on the other. Therefore, edge maps play a crucial role in quality assurance. Popular methods for computing edges are based on either the first derivative of the image as in FSL, or a variation of the Canny Edge detection method as implemented in AFNI. The crucial algorithmic parameter for adjustment for each of these methods relates to the image intensity. However, image intensity is relative and can be quite variable in most neuroimaging modalities. Further, the existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. We suggest that using the second derivative (difference of Gaussian, or DoG) of the image to generate edges resolves both these issues. This method primarily operates by specifying a spatial scale of interest (which is typically known in medical imaging) rather than a contrast scale, and creates closed surfaces by definition. We describe some convenient implementation features (for both efficiency and visual quality) developed here, and we provide open source implementations of this method as both online and high performance portable code. Finally, we include this method as part of both the AFNI and FSL software packages.
... rs-fMRI pre-processing. rs-fMRI images were pre-processed using AFNI 75 and FSL 65 . The first five volumes were discarded to ensure magnetic field saturation. ...
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Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from (i) resting-state functional imaging, (ii) diffusion tractography, (iii) microstructure covariance analysis, and (iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and function. MICA-MICs is available on the Canadian Open Neuroscience Platform data portal (https://portal.conp.ca) and the Open Science Framework (https://osf.io/j532r/).
... From these components, ten volumes of interest (VOIs) were selected, contributing to four key motor systems (17), namely the sensorimotor network, the motor initiation network, the basal ganglia network and the cerebellar network (Figure 1 A). For artifact removal, components' time courses were detrended, despiked using 3Ddespike (18), filtered by a fifth-order Butterworth low-pass filter (cut-off: 0.15 Hz), and normalized (19). ...
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Background: Differences in dopaminergic motor response in Parkinson's disease (PD) patients are related to specific PD subtypes. An important factor driving dopaminergic response might lie in the temporal dynamics in corticostriatal connections. Objectives: The aim of this study is to determine if altered resting-state dynamic functional network connectivity (dFNC) is associated with dopaminergic motor response. Methods: We assessed static and dFNC in 32 PD patients and 18 healthy controls (HC). Patients were subgrouped according to their dopaminergic motor response as low and high responders using a median split. Results: Patients featuring high dopaminergic responses spent more time in a regionally more integrated state 1 compared to HC. Furthermore, dFNC between aMCC/dACC (anterior midcingulate cortex/dorsal anterior cingulate cortex) and putamen was lower in low responders during a more segregated state 2 and correlated with dopaminergic motor response. Conclusions: Alterations in temporal dynamics of fronto-striatal connectivity might underlie treatment response in PD.
... Preprocessing was completed using a combination of FSL (5.0), AFNI (16.0.11) and the CompCor algorithm (niak-boss-0.13.0) (Cox, 1996;Behzadi et al., 2007;Jenkinson et al., 2012). Anatomical images were skull-stripped and segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). ...
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Purpose Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls. Methods We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05. Results We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04). Conclusion We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.
... Analysis of Functional Neuroimages [AFNI (Cox, 1996)] with alpha < 0.05; correction for 584 multiple comparisons was done using the AFNI non-parametric equitable thresholding and 585 clustering (ETAC) method (Cox, 2019), with 10 voxel-wise p-thresholds ranging from p = 0.01 586 to p = 0.001. To calculate the correlation between second language proficiency and grey matter 587 density in the pCC across all participants (n = 93), a single significant cluster from the group 588 analysis was used as a ROI; mean grey matter probability across all voxels within this ROI was 589 calculated for each participant. ...
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Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age- or disease-related neuroanatomical decline. While bilingualism is known to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between bilingual and monolingual older adults, matched on cognitive performance using a rigorous propensity score matching technique, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency in particular, were associated with lower grey matter integrity in a hub of the default mode network, a region that is particularly vulnerable to decline in aging and dementia, but preserved functional network organization that resembled the young adult brain. Our findings confirm that lifelong bilingualism contributes to CR through experience-dependent maintenance of optimal functional network structure of the domain-general attentional control network across the lifespan.
... Default Mode Network. DMN resting state functional MRI (rs-fMRI) data were processed using a combination of FreeSurfer (Fischl, Sereno, & Dale, 1999), AFNI (Cox, 1996), and FSL (Jenkinson et al., 2012), based primarily on the FSFAST processing stream (http://freesurfer.net/fswiki/FsFast). ...
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Approximately 10-30% of individuals with posttraumatic stress disorder (PTSD) exhibit a dissociative subtype of the condition defined by symptoms of depersonalization and derealization. This study examined the psychometric evidence for the dissociative subtype of PTSD in a sample of young, primarily male post-9/11-era Veterans (n = 374 at baseline and n = 163 at follow-up) and evaluated its biological correlates with respect to resting state functional connectivity (default mode network; DMN; n = 275), brain morphology (hippocampal subfield volume and cortical thickness; n = 280), neurocognitive functioning (n = 337), and genetic variation (n = 193). Multivariate analyses of PTSD and dissociation items suggested a class structure was superior to dimensional and hybrid ones, with 7.5% of the sample comprising the dissociative class; this group showed stability over 1.5 years. Covarying for age, sex, and PTSD severity, linear regression models revealed that derealization/depersonalization severity was associated with: decreased DMN connectivity between bilateral posterior cingulate cortex and right isthmus (p = .015; adjusted-p [padj] = .097); increased bilateral whole hippocampal, hippocampal head, and molecular layer head volume (p = .010 - .034; padj = .032 - .053); worse self-monitoring (p = .018; padj = .079); and a candidate genetic variant (rs263232) in the adenylyl cyclase 8 gene (p = .026), previously associated with dissociation. Results converged on biological structures and systems implicated in sensory integration, the neural representation of spatial awareness, and stress-related spatial learning and memory, suggesting possible mechanisms underlying the dissociative subtype of PTSD.
... All scanning took place at the Hospital for Sick Children (Toronto, Canada). Standard Analysis of Functional NeuroImages (AFNI, Cox, 1996;Cox and Hyde, 1997), FreeSurfer (Dale et al., 1999) and FMRIB Software Library (FSL, Jenkinson et al., 2012) tools were used to process fMRI data. T1-weighted images were skull stripped using FreeSurfer. ...
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Theory of Mind (ToM) is a core social cognitive skill that refers to the ability to attribute mental states to others. ToM involves understanding that others have beliefs, thoughts and desires that may be different from one's own and from reality. ToM is crucial to predict behaviour and navigate social interactions. This study employed the complementary methodological advantages of both functional MRI (fMRI) and magnetoencephalography (MEG) to examine the neural underpinnings of ToM in adults. Twenty healthy adults were first recruited to rate and describe 28 videos (15s long), each containing three moving shapes designed to depict either social interactions or random motion (control condition). The first sample of adults produced consistent narratives for 6 of those social videos and of those, 4 social videos and 4 control videos were chosen to include in the neuroimaging study. Another sample of twenty-five adults were then recruited to complete the neuroimaging in MEG and fMRI. In fMRI, we found increased activation in frontal-parietal regions in the social compared to the control condition corroborating previous fMRI findings. In MEG, we found recruitment of ToM networks in the social condition in theta, beta and gamma bands. The right supramarginal and angular gyri (right temporal parietal junction), right inferior parietal lobe and right temporal pole were recruited in the first 5s of the videos. Frontal regions such as the superior frontal gyrus were recruited in the second time window (5–10s). Brain regions such as the bilateral amygdalae were also recruited (5–10s), indicating that various social processes were integrated in understanding the social videos. Our study is one of the first to combine multi-modal neuroimaging to examine the neural networks underlying social cognitive processes, combining the strengths of the spatial resolution of fMRI and temporal resolution of MEG. Understanding this information from both modalities helped delineate the mechanism by which ToM processing unfolds over time in healthy adults. This allows us to determine a benchmark against which clinical populations can be compared.
... Taking neuroimaging data processing as an example, some sophisticated neuroimaging processing tools (e.g., AFNI (Cox, 1996), FSL (Jenkinson et al., 2012), ANTs (Avants et al., 2008) , SPM , FreeSurfer (Fischl, 2012), and Nipy (Millman and Brett, 2007) ) have been designed to analyze multimodal imaging data. ...
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In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic “Frontiers in Neuroinformatics” according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.
... First, Analysis of Functional NeuroImages' (AFNI, version 18.0.05) 48 3dPeriodogram was used to transform the preprocessed fourdimensional MREG data into a voxel-wise FFT spectrum. For respiratory and cardiac frequency estimation, the fourth ventricle and anterior/middle cerebral arteries, respectively, were chosen as reference points, as the investigated physiological events in the MREG FFT spectrum were visually most pronounced in these , and cardiac (photoplethysmogram) signals are transformed into frequency spectra with fast Fourier transformation (FFT). ...
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Background Narcolepsy is a chronic neurological disease characterized by daytime sleep attacks, cataplexy, and fragmented sleep. The disease is hypothesized to arise from destruction or dysfunction of hypothalamic hypocretin-producing cells that innervate wake-promoting systems including the ascending arousal network (AAN), which regulates arousal via release of neurotransmitters like noradrenalin. Brain pulsations are thought to drive intracranial cerebrospinal fluid flow linked to brain metabolite transfer that sustains homeostasis. This flow increases in sleep and is suppressed by noradrenalin in the awake state. Here we tested the hypothesis that narcolepsy is associated with altered brain pulsations, and if these pulsations can differentiate narcolepsy type 1 from healthy controls. Methods In this case-control study, 23 patients with narcolepsy type 1 (NT1) were imaged with ultrafast fMRI (MREG) along with 23 age- and sex-matched healthy controls (HC). The physiological brain pulsations were quantified as the frequency-wise signal variance. Clinical relevance of the pulsations was investigated with correlation and receiving operating characteristic analysis. Results We find that variance and fractional variance in the very low frequency (MREGvlf) band are greater in NT1 compared to HC, while cardiac (MREGcard) and respiratory band variances are lower. Interestingly, these pulsations differences are prominent in the AAN region. We further find that fractional variance in MREGvlf shows promise as an effective bi-classification metric (AUC = 81.4%/78.5%), and that disease severity measured with narcolepsy severity score correlates with MREGcard variance (R = −0.48, p = 0.0249). Conclusions We suggest that our novel results reflect impaired CSF dynamics that may be linked to altered glymphatic circulation in narcolepsy type 1.
... To identify electrodes located within the posterior cingulate cortex, a post-operative CT scan was co-registered to a pre-operative T1 anatomical MRI scan for each subject, using FSL and AFNI (Cox, 1996;Jenkinson et al., 2012). The volume location of each electrode was identified by clear hyperintensities on the aligned CT using AFNI and visualized using iELVis software functions in MATLAB (v2016a, MathWorks, MA, USA) (Groppe et al., 2017). ...
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Posterior cingulate cortex (PCC) is an enigmatic region implicated in psychiatric and neurological disease, yet its role in cognition remains unclear. Human studies link PCC to episodic memory and default mode network (DMN), while findings from the non-human primate emphasize executive processes more associated with the cognitive control network (CCN) in humans. We hypothesized this difference reflects an important functional division between dorsal (executive) and ventral (episodic) PCC. To test this, we utilized human intracranial recordings of population and single unit activity targeting dorsal PCC during an alternated executive/episodic processing task. Dorsal PCC population responses were significantly enhanced for executive, compared to episodic, task conditions, consistent with the CCN. Single unit recordings, however, revealed four distinct functional types with unique executive (CCN) or episodic (DMN) response profiles. Our findings provide critical electrophysiological data from human PCC, bridging incongruent views within and across species, furthering our understanding of PCC function.
... We applied well-accepted toolboxes, AFNI [31] (for ADNI) and DPARSF [32] (for ABIDE and OASIS), to perform a standardized preprocessing procedure for fMRI data. In particular, the first 5 or 10 volumes of each image are discarded due to potential non-equilibrium magnetization. ...
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Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnoses of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling across multiple spatial scales, namely "Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers of graph convolution and the atlas-guided pooling, for a comprehensive extraction of diagnostic information from multiscale FCNs. Experiments on neuroimaging data from 1792 subjects demonstrate the effectiveness of our proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All results show significant advantages of our proposed method over other competing methods. This study not only demonstrates the feasibility of brain disorder diagnosis using resting-state fMRI empowered by deep learning, but also highlights that the functional interactions in the multiscale brain hierarchy are worth being explored and integrated into deep learning network architectures for better understanding the neuropathology of brain disorders.
... For the left and right pSTS ROIs, we averaged and combined the time series to form a single bilateral ROI time series. We characterized group differences in voxel-wise connectivity across the brain using AFNI's 3dMVM program (Cox, 1996). Given our primary research aims, we focused on contrasting voxel-wise connectivity between the AQ+ and AQ-DP groups. ...
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Autism traits are common exclusionary criteria in developmental prosopagnosia (DP) studies. We investigated whether autism traits produce qualitatively different face processing in 43 DPs with high vs. low autism quotient (AQ) scores. Compared to controls (n = 27), face memory and perception were similarly deficient in the high- and low-AQ DPs, with the high-AQ DP group additionally showing deficient face emotion recognition. Task-based fMRI revealed reduced occipito-temporal face selectivity in both groups, with high-AQ DPs additionally demonstrating decreased posterior superior temporal sulcus selectivity. Resting-state fMRI showed similar reduced face-selective network connectivity in both DP groups compared with controls. Together, this demonstrates that high- and low-AQ DP groups have very similar face processing deficits, with additional facial emotion deficits in high-AQ DPs.
Article
Hierarchies form critical scaffolds for top-down processing but are often multiplex. In the brain, multiple layers of complex hierarchies intersect, dissociate, and re-converge over the lifespan. Although aspects of local hierarchical organizations are well-mapped for sensory systems, the fashion by which hierarchical organization extends globally is unknown. Human neuroimaging provides a means by which to observe both the developmental emergence and functions of global neurohierarchical organization. Here, we leveraged these advances to distill multiple layers of hierarchical formation across diverse brain-tissue quantifications. We demonstrate that these layers form common and dissociable biomarkers of the developmental emergence of complex cognition. Our results indicate that multiplex neurocognitive development both processes across a normative hierarchical pattern and contributes to engraining the pattern into cortical function. Further, our results suggest that neurocognitive development is largely contemporaneous with neurocognitive aging in an integrated, flexible lifespan sequence.
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Objective This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps. Methods Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject. Results fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( p = 4.88×10 –4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data. Conclusion LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.
Article
How do humans excel at tracking the narrative of a particular speaker with a distracting noisy background? This feat places great demands on the collaboration between speech processing and goal-related regulatory functions. Here, we propose that separate subsystems with different cross-task dynamic activity properties and distinct functional purposes support goal-directed speech listening. We adopted a naturalistic dichotic speech listening paradigm in which listeners were instructed to attend to only one narrative from two competing inputs. Using functional magnetic resonance imaging with inter- and intra-subject correlation techniques, we discovered a dissociation in response consistency in temporal, parietal and frontal brain areas as the task demand varied. Specifically, some areas in the bilateral temporal cortex (SomMotB_Aud and TempPar) and lateral prefrontal cortex (DefaultB_PFCl and ContA_PFCl) always showed consistent activation across subjects and across scan runs, regardless of the task demand. In contrast, some areas in the parietal cortex (DefaultA_pCunPCC and ContC_pCun) responded reliably only when the task goal remained the same. These results suggested two dissociated functional neural networks that were independently validated by performing a data-driven clustering analysis of voxelwise functional connectivity patterns. A subsequent meta-analysis revealed distinct functional profiles for these two brain correlation maps. The different-task correlation map was strongly associated with language-related processes (e.g., listening, speech and sentences), whereas the same-task versus different-task correlation map was linked to self-referencing functions (e.g., default mode, theory of mind and autobiographical topics). Altogether, the three-pronged findings revealed two anatomically and functionally dissociated subsystems supporting goal-directed speech listening.
Article
The vagus nerve projects to a well-defined neural circuit via the nucleus tractus solitarii (NTS) and its stimulation elicits a wide range of metabolic, neuromodulatory, and behavioral effects. Transcutaneous vagus nerve stimulation (tVNS) has been established as a promising technique to non-invasively alter brain function. However, the precise dynamics elicited by tVNS in humans are still largely unknown. Here, we performed fMRI with concurrent right-sided tVNS (vs. sham) following a randomized cross-over design (N = 40). First, to unravel the temporal profile of tVNS-induced changes in the NTS, we compared fMRI time series to canonical profiles for stimulation ON and OFF cycles. Model comparisons indicated that NTS time series were best fit by block-wise shifts in signal amplitude with stimulation ON and OFF estimates being highly correlated. Therefore, we compared stimulation (ON + OFF) versus baseline phases and found that tVNS increased fMRI BOLD activation in the NTS, but this effect was dependent on sufficient temporal signal-to-noise ratio (tSNR) in the mask. Second, to identify the spatiotemporal evolution of tVNS-induced changes in the brain, we examined lagged co-activation patterns and phase coherence. In contrast to our hypothesis, tVNS did not alter dynamic functional connectivity after correction for multiple comparisons. Third, to establish a positive control for future research, we measured changes in gastric myoelectrical frequency via an electrogastrogram. Again, in contrast to our hypothesis, tVNS induced no changes in gastric frequency. Collectively, our study provides evidence that tVNS can perturb brain signaling in the NTS, but these effects are dependent on tSNR and require precise localization. In light of an absence of acute tVNS-induced effects on dynamic functional connectivity and gastric motility, we discuss which steps are necessary to advance future research on afferent and efferent effects of tVNS.
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The brain is a highly organized, dynamic system whose network architecture is often assessed through resting functional magnetic resonance imaging (fMRI) functional connectivity. The functional interactions between brain areas, including those observed during rest, are assumed to stem from the collective influence of action potentials carried by long-range neural projections. However, the contribution of individual neurons to brain-wide functional connectivity has not been systematically assessed. Here we developed a method to concurrently measure and compare the spiking activity of local neurons with fMRI signals measured across the brain during rest. We recorded spontaneous activity from neural populations in cortical face patches in the macaque during fMRI scanning sessions. Individual cells exhibited prominent, bilateral coupling with fMRI fluctuations in a restricted set of cortical areas inside and outside the face patch network, partially matching the pattern of known anatomical projections. Within each face patch population, a subset of neurons was positively coupled with the face patch network and another was negatively coupled. The same cells showed inverse correlations with distinct subcortical structures, most notably the lateral geniculate nucleus and brainstem neuromodulatory centers. Corresponding connectivity maps derived from fMRI seeds and local field potentials differed from the single unit maps, particularly in subcortical areas. Together, the results demonstrate that the spiking fluctuations of neurons are selectively coupled with discrete brain regions, with the coupling governed in part by anatomical network connections and in part by indirect neuromodulatory pathways.
Article
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Functional gradients, in which response properties change gradually across the cortical surface, have been proposed as a key organising principle of the brain. However, the presence of these gradients remains undetermined in many brain regions. Resting-state neuroimaging studies have suggested these gradients can be reconstructed from patterns of functional connectivity. Here we investigate the accuracy of these reconstructions and establish whether it is connectivity or the functional properties within a region that determine these “connectopic maps”. Different manifold learning techniques were used to recover visual field maps while participants were at rest or engaged in natural viewing. We benchmarked these reconstructions against maps measured by traditional visual field mapping. We report an initial exploratory experiment of a publicly available naturalistic imaging dataset, followed by a preregistered replication using larger resting-state and naturalistic imaging datasets from the Human Connectome Project. Connectopic mapping accurately predicted visual field maps in primary visual cortex, with better predictions for eccentricity than polar angle maps. Non-linear manifold learning methods outperformed simpler linear embeddings. We also found more accurate predictions during natural viewing compared to resting-state. Varying the source of the connectivity estimates had minimal impact on the connectopic maps, suggesting the key factor is the functional topography within a brain region. The application of these standardised methods for connectopic mapping will allow the discovery of functional gradients across the brain. Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on 19 April 2022. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.19771717.
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Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are prevalent among military populations, and both have been associated with working memory (WM) impairments. Previous resting-state functional connectivity (rsFC) research conducted separately in PTSD and mTBI populations suggests that there may be similar and distinct abnormalities in WM-related networks. However, no studies have compared rsFC of WM brain regions in participants with mTBI versus PTSD. We used resting-state fMRI to investigate rsFC of WM networks in U.S. Service Members (n = 127; ages 18–59) with mTBI only (n = 46), PTSD only (n = 24), and an orthopedically injured (OI) control group (n = 57). We conducted voxelwise rsFC analyses with WM brain regions to test for differences in WM network connectivity in mTBI versus PTSD. Results revealed reduced rsFC between ventrolateral prefrontal cortex (vlPFC), lateral premotor cortex, and dorsolateral prefrontal cortex (dlPFC) WM regions and brain regions in the dorsal attention and somatomotor networks in both mTBI and PTSD groups versus controls. When compared to those with mTBI, individuals with PTSD had lower rsFC between both the lateral premotor WM seed region and middle occipital gyrus as well as between the dlPFC WM seed region and paracentral lobule. Interestingly, only vlPFC connectivity was significantly associated with WM performance across the samples. In conclusion, we found primarily overlapping patterns of reduced rsFC in WM brain regions in both mTBI and PTSD groups. Our finding of decreased vlPFC connectivity associated with WM is consistent with previous clinical and neuroimaging studies. Overall, these results provide support for shared neural substrates of WM in individuals with either mTBI or PTSD.
Article
Background: Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown. Purpose: To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI). Study type: Prospective. Population: A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women). Field strength/sequence: Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT. Assessment: All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n = 58: 28 PWH, 30 SN; nonadaptive training, n = 48: 25 PWH, 23 SN), 25 sessions over 5-8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2. Statistical tests: Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction. Results: The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = -0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%). Data conclusion: ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH. Evidence level: 1 TECHNICAL EFFICACY: 1.
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Hallucinations are a core feature of psychosis and common in Parkinson’s. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson’s-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson’s-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson’s-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing. The examination of temporal dynamics in Parkinson’s-hallucinations reveals that the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations.
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The ability to learn associations between events is critical for everyday functioning (e.g., decision making, social interactions) and has been attributed to structural differences in white matter tracts connecting cortical regions to the hippocampus (e.g., fornix) and striatum (e.g., internal capsule) in younger-old adults (ages 65-85 years). However, evidence of associative learning has not been assessed within oldest-old adults (ages 90+ years), despite its relevance to other extensively characterized cognitive abilities in the oldest-old and the relatively large effect of advanced age on the microstructural composition of these white matter tracts. We acquired multicompartment diffusion-weighted magnetic resonance imaging data from 22 oldest-old adults without dementia (mean age = 92.91 ± 1.44 years) who also completed an associative learning task. Behavioral results revealed significantly better associative learning performance during later task stages, as expected if participants incidentally learned the cue-cue-target associations for frequently occurring event triplets. Moreover, better learning performance was significantly predicted by better microstructure of cortico-striatal white matter (posterior limb of the internal capsule). Finding that associative learning abilities in the 10th decade of life are supported by better microstructure of white matter tracts connecting the cortex to the striatum underscores their importance to learning performance across the entire lifespan.
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Purpose: Multiple data formats in the MRS community currently hinder data sharing and integration. NIfTI-MRS is proposed as a standard spectroscopy data format, implemented as an extension to the Neuroimaging informatics technology initiative (NIfTI) format. This standardized format can facilitate data sharing and algorithm development as well as ease integration of MRS analysis alongside other imaging modalities. Methods: A file format using the NIfTI header extension framework incorporates essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to convert single-voxel and spectroscopic imaging data to NIfTI-MRS. Visualization of data in NIfTI-MRS is provided by development of a dedicated plugin for FSLeyes, the FMRIB Software Library (FSL) image viewer. Results: Online documentation and 10 example datasets in the proposed format are provided. Code examples of NIfTI-MRS readers are implemented in common programming languages. Conversion software, spec2nii, currently converts 14 formats where data is stored in image-space to NIfTI-MRS, including Digital Imaging and Communications in Medicine (DICOM) and vendor proprietary formats. Conclusion: NIfTI-MRS aims to solve issues arising from multiple data formats being used in the MRS community. Through a single conversion point, processing and analysis of MRS data are simplified, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. Greater standardization and harmonization become possible. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, for visualization.
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Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Optimal conditions for resting-state functional magnetic resonance imaging (rs-fMRI) are still highly debated. Here, we comprehensively assessed the effects of various rest conditions on all cortical resting-state networks (RSNs) defined by an established atlas. Twenty-two healthy college students (22 ± 4 years old, 12 females) were scanned on a GE 3T MRI scanner. Rs-fMRI datasets were collected under four different conditions for each subject: (1) eyes open in dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), (3) eyes closed while remembering four numbers through the scan session (Eyes-Closed-Number) and (4) asked to watch a movie (Movie). We completed a thorough examination of the 17 functional RSNs defined by Yeo and colleagues. Importantly, the movie led to changes in global connectivity and should be avoided as a rest condition. Conversely, there were no significant connectivity differences between conditions within the frontoparietal control and limbic networks and the following subnetworks as defined by Yeo et al.: default-B, dorsal-attention-B and salience/ventral-attention-B. These were not even significant when compared to the highly stimulative Movie condition. A significant difference was not found between Eyes-Closed and Eyes-Closed-Number conditions in whole-brain, within-network and between-network comparisons. When considering other rest conditions, however, we observed connectivity changes in some subnetworks, including those of the default-mode-network. Overall, we found conditions with more external stimulation led to more changes in functional connectivity during rs-fMRI. In conclusion, the comprehensive results of our study can aid in choosing rest conditions for the study of overall and specific functional networks.
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Background and purpose: A number of functional magnetic resonance imaging (fMRI) studies rely on application of anesthetic agents during scanning that can modulate and complicate interpretation of the measured hemodynamic blood oxygenation level-dependent (BOLD) response. The purpose of the present study was to investigate the effect of general anesthesia on two main components of BOLD signal including neuronal activity and vascular response. Methods: Breath-holding (BH) fMRI was conducted in wakefulness and under anesthesia states in 9 patients with drug-resistant epilepsy who needed to get scanned under anesthesia during laser interstitial thermal therapy. BOLD and BOLD cerebrovascular reactivity (BOLD-CVR) maps were compared using t-test between two states to assess the effect of anesthesia on neuronal activity and vascular factors (p < .05). Results: Overall, our findings revealed an increase in BOLD-CVR and decrease in BOLD response under anesthesia in several brain regions. The results proposed that the modulatory mechanism of anesthetics on neuronal and vascular components of BOLD signal may work in different ways. Conclusion: This experiment for the first human study showed that anesthesia may play an important role in dissociation between neuronal and vascular responses contributed to hemodynamic BOLD signal using BH fMRI imaging that may assist the implication of general anesthesia and interpretation of outcomes in clinical setting.
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Aging is associated with structural and functional changes in the brain, with a decline in cognitive functions observed as its inevitable concomitant. The body of literature suggests dopamine and noradrenaline as prominent candidate neuromodulators to mediate these effects; however, knowledge regarding the underlying mechanisms is scarce. To fill this gap, we compared resting-state functional connectivity (FC) patterns of ventral tegmental area (VTA), substantia nigra pars compacta (SNc) and locus coeruleus (LC) in healthy young (20–35 years; N = 37) and older adults (55–80 years; N = 27). Additionally, we sought FC patterns of these structures associated with performance in tasks probing executive, attentional and reward functioning, and we compared the functional coupling of the bilateral SNc. The results showed that individual SNc had stronger coupling with ipsilateral cortical and subcortical areas along with the contralateral cerebellum in the whole sample, and that the strength of connections of this structure with angular gyrus and lateral orbitofrontal cortex predicted visuomotor search abilities. In turn, older age was associated with greater local synchronization within VTA, its lower FC with caudate, mediodorsal thalamus, and SNc, as well as higher FC of both midbrain dopaminergic seeds with red nuclei. LC functional coupling showed no differences between the groups and was not associated with any of the behavioral functions. To the best of our knowledge, this work is the first to report the age-related effects on VTA local synchronization and its connectivity with key recipients of dopaminergic innervation, such as striatum and mediodorsal thalamus.
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Functional connectivity between the amygdala and the medial prefrontal cortex (mPFC) has been identified as a neural substrate of emotion regulation that undergoes changes throughout development. Amygdala-mPFC connectivity has been well studied in adolescents and adults, with a mature profile typically emerging at 10 years of age. Maternal bonding in childhood has been shown to buffer amygdala reactivity and to influence the trajectory of amygdala-mPFC coupling, which in turn may impact socio-emotional dysfunction later in life. The oxytocinergic system is critical in the development of social behavior and maternal bonding. Early life parental care influences the methylation status of the oxytocin receptor (OXTRm) in animal models and humans, and higher OXTRm is associated with lower amygdala-PFC functional connectivity in adults. Using a neuroimaging-epigenetic approach, we investigated OXTRm as a biological marker of functional connectivity maturation in middle childhood. We find that higher levels of OXTRm are associated with a more adult-like functional connectivity profile. We also find that lower OXTRm blunts the association between amygdala-mPFC connectivity and future internalizing behaviors in early adolescence. These findings implicate OXTRm as a biological marker at the interface of the social environment and amygdala-mPFC coupling in emotional and behavioral regulation. Ultimately, identification of neurobiological markers may lead to earlier detection of children at risk for socio-emotional dysfunction.
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Common research practices in neuroimaging studies using functional magnetic resonance imaging may produce outcomes that are difficult to replicate. Results that cannot be replicated have contributed to a replication crisis in psychology, neuroscience, and other disciplines over the years. Here we replicate two previous papers in which the authors present two analysis paths for a dataset in which participants underwent fMRI while performing a recognition memory test for old and new words. Both studies found activation in the medial temporal lobe including the hippocampus, with the first demonstrating a distinction in activation corresponding to true and perceived oldness of stimuli and the second demonstrating that activation reflects the subjective experience of the participant. We replicated the behavioral and MRI acquisition parameters reported in the two target articles (Daselaar et al., 2006; Daselaar et al., 2006) with N = 53 participants. We focused fMRI analyses on regions of interest reported in the target articles examining fMRI activation for differences corresponding with true and perceived oldness and those associated with the subjective memory experiences of recollection, familiarity, and novelty. Comparisons between true and perceived oldness revealed main effects not only for true, but also perceived oldness along with a significant interaction. We replicate the findings of recollection and familiarity signals in the hippocampus and medial temporal lobe cortex, respectively, but failed to replicate a novelty signal in the anterior medial temporal lobe. These results remained when we analyzed only correct trials, indicating that the effects were not due to selectively averaging correct and incorrect trials. Taken together, our findings demonstrate that activation in the hippocampus corresponds to the subjective experience associated with correct recognition memory retrieval.
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Study Objectives This study investigated the altered neural function involved in emotional interference and its role in linking sleep disturbance and depressive/anxiety symptoms in rotating shift workers. Methods Sixty rotating shift workers and 61 controls performed the emotional Stroop task in three blocks (emotional-related, sleep-related, and neutral words) during functional magnetic resonance imaging (fMRI) assessments. Sleep disturbance and depressive/anxiety symptoms were assessed using self-report measures and sleep diaries. Actigraphy was used to assess the sleep and circadian variables. fMRI scans were performed to compare brain activation during the emotional Stroop task. The proposed moderating models were tested using the PROCESS macro in SPSS software. Results A significant condition effect on reaction time was detected. Regardless of the group, reaction times were longer in the negative emotional word and sleep-related conditions than in the neutral word condition. Whole-brain analysis revealed that rotating shift workers show greater neural activation in the left dorsolateral prefrontal cortex (DLPFC) compared with controls while performing the emotional Stroop task with negative emotional words. Sleep disturbance was more strongly associated with depressive symptoms when activation of the left DLPFC was higher during the emotional Stroop task with negative words. Conclusions The left DLPFC may play important roles in increased sensitivity to emotional information, possibly due to circadian misalignment, and has moderating effects on the association between sleep disturbance and depressive symptoms in rotating shift workers. These findings will help to identify possible brain regions where interventions can be performed to correct sleep and mood problems in rotating shift workers.
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Neuronal activity causes local changes in cerebral blood flow, blood volume, and blood oxygenation. Magnetic resonance imaging (MRI) techniques sensitive to changes in cerebral blood flow and blood oxygenation were developed by high-speed echo planar imaging. These techniques were used to obtain completely noninvasive tomographic maps of human brain activity, by using visual and motor stimulus paradigms. Changes in blood oxygenation were detected by using a gradient echo (GE) imaging sequence sensitive to the paramagnetic state of deoxygenated hemoglobin. Blood flow changes were evaluated by a spin-echo inversion recovery (IR), tissue relaxation parameter T1-sensitive pulse sequence. A series of images were acquired continuously with the same imaging pulse sequence (either GE or IR) during task activation. Cine display of subtraction images (activated minus baseline) directly demonstrates activity-induced changes in brain MR signal observed at a temporal resolution of seconds. During 8-Hz patterned-flash photic stimulation, a significant increase in signal intensity (paired t test; P less than 0.001) of 1.8% +/- 0.8% (GE) and 1.8% +/- 0.9% (IR) was observed in the primary visual cortex (V1) of seven normal volunteers. The mean rise-time constant of the signal change was 4.4 +/- 2.2 s for the GE images and 8.9 +/- 2.8 s for the IR images. The stimulation frequency dependence of visual activation agrees with previous positron emission tomography observations, with the largest MR signal response occurring at 8 Hz. Similar signal changes were observed within the human primary motor cortex (M1) during a hand squeezing task and in animal models of increased blood flow by hypercapnia. By using intrinsic blood-tissue contrast, functional MRI opens a spatial-temporal window onto individual brain physiology.
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We report that visual stimulation produces an easily detectable (5-20%) transient increase in the intensity of water proton magnetic resonance signals in human primary visual cortex in gradient echo images at 4-T magnetic-field strength. The observed changes predominantly occur in areas containing gray matter and can be used to produce high-spatial-resolution functional brain maps in humans. Reducing the image-acquisition echo time from 40 msec to 8 msec reduces the amplitude of the fractional signal change, suggesting that it is produced by a change in apparent transverse relaxation time T*2. The amplitude, sign, and echo-time dependence of these intrinsic signal changes are consistent with the idea that neural activation increases regional cerebral blood flow and concomitantly increases venous-blood oxygenation.
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Knowledge of regional cerebral hemodynamics has widespread application for both physiological research and clinical assessment because of the well-established interrelation between physiological function, energy metabolism, and localized blood supply. A magnetic resonance technique was developed for quantitative imaging of cerebral hemodynamics, allowing for measurement of regional cerebral blood volume during resting and activated cognitive states. This technique was used to generate the first functional magnetic resonance maps of human task activation, by using a visual stimulus paradigm. During photic stimulation, localized increases in blood volume (32 +/- 10 percent, n = 7 subjects) were detected in the primary visual cortex. Center-of-mass coordinates and linear extents of brain activation within the plane of the calcarine fissure are reported.
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We report that visual stimulation produces an easily detectable (5-20%) transient increase in the intensity of water proton magnetic resonance signals in human primary visual cortex in gradient echo images at 4-T magnetic-field strength. The observed changes predominantly occur in areas containing gray matter and can be used to produce high-spatial-resolution functional brain maps in humans. Reducing the image-acquisition echo time from 40 msec to 8 msec reduces the amplitude of the fractional signal change, suggesting that it is produced by a change in apparent transverse relaxation time T*2. The amplitude, sign, and echo-time dependence of these intrinsic signal changes are consistent with the idea that neural activation increases regional cerebral blood flow and concomitantly increases venous-blood oxygenation.
A new method of two- or three-dimensional spin density imaging by nuclear magnetic resonance (NMR) is proposed, which exploits the properties of spin echoes in time-dependent magnetic field gradients. An analysis shows that simultaneous observation and differentiation of signals, arising from all spins distributed in a plane or set of planes within the specimen, is possible. The method is thus capable of producing visual pictures faster than previously described planar imaging methods.
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New techniques of functional brain mapping using magnetic resonance imaging (fMRI) are described. Methods monitoring blood oxygenation and blood flow changes are distinguished and compared. The major sources of artifact, subject motion and contamination of the functional image by large veins, are discussed, and methods of data analysis are reviewed. A survey of the numerous preliminary fMRI studies in cognitive neuroscience is presented, with specific attention to recent studies of language areas in deaf subjects and the cortical effects of motor training.
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Using gradient-echo echo-planar MRI, a local signal increase of 4.3 ± 0.3% is observed in the human brain during task activation, suggesting a local decrease in blood deoxyhemoglobin concentration and an increase in blood oxygenation. Images highlighting areas of signal enhancement temporally correlated to the task are created. © 1992 Academic Press, Inc.
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Several aspects of blipped echo-planar imaging (EPI) are treated mathematically. An expression relating the necessary readout gradient strength and sampling time to the spatial resolution and readout duration is derived. It is shown how the net spatial resolution may be limited by the object's T2 characteristics and Bo field homogeneity, irrespective of the number of sampled points. Additionally, off-resonance effects result in a loss of spatial resolution and image distortion to a considerably greater degree than in conventional two-dimensional Fourier transform imaging. The extent of these effects is directly related to the time required to acquire the data matrix, and is therefore amplified when EPI is implemented on a standard commercial whole-body system which because of limited gradient performance uses necessarily longer sampling durations. Specific hardware modifications to a standard commercial imager are considered to allow successful EPI implementation. EPI image characteristics are compared quantitatively with those of conventional methods. © 1990 Academic Press, Inc.
Article
Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor—cortex and compared with more conventional image—subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.
Article
Using gradient-echo echo-planar MRI, a local signal increase of 4.3 +/- 0.3% is observed in the human brain during task activation, suggesting a local decrease in blood deoxyhemoglobin concentration and an increase in blood oxygenation. Images highlighting areas of signal enhancement temporally correlated to the task are created.
Article
Paramagnetic deoxyhemoglobin in venous blood is a naturally occurring contrast agent for magnetic resonance imaging (MRI). By accentuating the effects of this agent through the use of gradient-echo techniques in high fields, we demonstrate in vivo images of brain microvasculature with image contrast reflecting the blood oxygen level. This blood oxygenation level-dependent (BOLD) contrast follows blood oxygen changes induced by anesthetics, by insulin-induced hypoglycemia, and by inhaled gas mixtures that alter metabolic demand or blood flow. The results suggest that BOLD contrast can be used to provide in vivo real-time maps of blood oxygenation in the brain under normal physiological conditions. BOLD contrast adds an additional feature to magnetic resonance imaging and complements other techniques that are attempting to provide positron emission tomography-like measurements related to regional neural activity.
Article
Several aspects of blipped echo-planar imaging (EPI) are treated mathematically. An expression relating the necessary readout gradient strength and sampling time to the spatial resolution and readout duration is derived. It is shown how the net spatial resolution may be limited by the object's T2 characteristics and B0 field homogeneity, irrespective of the number of sampled points. Additionally, off-resonance effects result in a loss of spatial resolution and image distortion to a considerably greater degree than in conventional two-dimensional Fourier transform imaging. The extent of these effects is directly related to the time required to acquire the data matrix, and is therefore amplified when EPI is implemented on a standard commercial whole-body system which because of limited gradient performance uses necessarily longer sampling durations. Specific hardware modifications to a standard commercial imager are considered to allow successful EPI implementation. EPI image characteristics are compared quantitatively with those of conventional methods.
Article
Fast low-angle shot (FLASH) imaging enables T1-weighted scans to be acquired in a few seconds. However, the diagnostic image quality is severely compromised by the appearance of artifactual bands parallel to the frequency encode direction. We show that the band structure arises from differences in the ability of the phase encode gradient to spoil transverse coherences that build up between successive repetition intervals. A theoretical understanding of the mechanisms involved leads to a comparison between various methods of spoiling the unwanted echoes throughout the whole field of view. Spoiler gradients whose amplitudes change linearly with phase encode step number are treated in detail. The theory predicts that the spoilers will rotate and rescale the band structure and these results are confirmed experimentally. The effect of the spoilers at a given location along the gradient is equivalent to the effect on the entire field of view of an incremented phase shift applied to the radiofrequency pulse. An appropriate rf phase shift scheme should therefore provide ideal spoiling characteristics for FLASH imaging.
Article
In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
Article
Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor-cortex and compared with more conventional image-subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.
Echo-planar imaging of the human brain using a three axis local gradient coil
  • E C Wong
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Co-Planar Stereotaxic Atlas of the Human Brain Thieme Medical Publishers Automatic 3D intersubject registra-tion of MR volumetric data in standardized Talairach space
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Anatomic variability as measured with a 3D reconstructed Talairach atlas
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Brain magnetic resonance imaging with contrast dependent o n b l o o d o xygenation
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